Probabilistic model-building algorithms (PMBGAs), also called estimation of distribution algorithms (EDAs) and iterated density estimation algorithms (IDEAs), replace traditional variation of genetic and evolutionary algorithms by (1) building a probabilistic model of promising solutions and (2) sampling the built model to generate new candidate solutions. PMBGAs are also known as estimation of distribution algorithms (EDAs) and iterated density-estimation algorithms (IDEAs).
Replacing traditional crossover and mutation operators by building and sampling a probabilistic model of promising solutions enables the use of machine learning techniques for automatic discovery of problem regularities and exploitation of these regularities for effective exploration of the search space. Using machine learning in optimization enables the design of optimization techniques that can automatically adapt to the given problem. There are many successful applications of PMBGAs, for example, Ising spin glasses in 2D and 3D, graph partitioning, MAXSAT, feature subset selection, forest management, groundwater remediation design, telecommunication network design, antenna design, and scheduling.
This tutorial provides a gentle introduction to PMBGAs with an overview of major research directions in this area. Strengths and weaknesses of different PMBGAs will be discussed and suggestions will be provided to help practitioners to choose the best PMBGA for their problem.
The video of this tutorial presented at GECCO-2008 can be found at
http://medal.cs.umsl.edu/blog/?p=293
Radial basis function network ppt bySheetal,Samreen and Dhanashrisheetal katkar
Radial Basis Functions are nonlinear activation functions used by artificial neural networks.Explained commonly used RBFs ,cover's theorem,interpolation problem and learning strategies.
In Comparison with other object detection algorithms, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once.
This presentation discusses the following Fuzzy logic concepts:
Introduction
Crisp Variables
Fuzzy Variables
Fuzzy Logic Operators
Fuzzy Control
Case Study
Slides by Míriam Bellver at the UPC Reading group for the paper:
Liu, Wei, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, and Scott Reed. "SSD: Single Shot MultiBox Detector." ECCV 2016.
Full listing of papers at:
https://github.com/imatge-upc/readcv/blob/master/README.md
Radial basis function network ppt bySheetal,Samreen and Dhanashrisheetal katkar
Radial Basis Functions are nonlinear activation functions used by artificial neural networks.Explained commonly used RBFs ,cover's theorem,interpolation problem and learning strategies.
In Comparison with other object detection algorithms, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once.
This presentation discusses the following Fuzzy logic concepts:
Introduction
Crisp Variables
Fuzzy Variables
Fuzzy Logic Operators
Fuzzy Control
Case Study
Slides by Míriam Bellver at the UPC Reading group for the paper:
Liu, Wei, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, and Scott Reed. "SSD: Single Shot MultiBox Detector." ECCV 2016.
Full listing of papers at:
https://github.com/imatge-upc/readcv/blob/master/README.md
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
Slides reviewing the paper:
Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is all you need." In Advances in Neural Information Processing Systems, pp. 6000-6010. 2017.
The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.
Reinforcement Learning 5. Monte Carlo MethodsSeung Jae Lee
A summary of Chapter 5: Monte Carlo Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
Topological Data Analysis: visual presentation of multidimensional data setsDataRefiner
Topology data analysis (TDA) is an unsupervised approach which may revolutionise the way data can be mined and eventually drive the new generation of analytical tools. The idea behind TDA is an attempt to "measure" shape of data and find compressed combinatorial representation of the shape. In ordinary topology, the combinatorial representations serve the purpose of providing the compressed representation of high dimensional data sets which retains information about the geometric relationships between data points. TDA can also be used as a very powerful clustering technique. Edward will present the comparison between TDA and other dimension reduction algorithms like PCA, LLE, Isomap, MDS, and Spectral Embedding.
An Neural Network (NN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.
It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.
An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.
An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for particular input patterns.
In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output.
Real Time Object Detection System with YOLO and CNN Models: A ReviewSpringer
The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK
ONCE (YOLO) algorithm and it's more evolved versions are briefly described in this research survey. This
survey is all about YOLO and convolution neural networks (CNN) in the direction of real time object detection.
YOLO does generalized object representation more effectively without precision losses than other object
detection models. CNN architecture models have the ability to eliminate highlights and identify objects in any
given image. When implemented appropriately, CNN models can address issues like deformity diagnosis,
creating educational or instructive application, etc. This article reached at number of observations and
perspective findings through the analysis. Also it provides support for the focused visual information and
feature extraction in the financial and other industries, highlights the method of target detection and feature
selection, and briefly describes the development process of yolo algorithm
Survey for recursive neural networks. Including recursive neural network (RNN), recursive autoencoder (RAE), unfolding RAE & dynamic pooling, matrix-vector RNN (MV-RNN), and recursive neural tensor network (RNTN), published by Socher et al.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
Slides reviewing the paper:
Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. "Attention is all you need." In Advances in Neural Information Processing Systems, pp. 6000-6010. 2017.
The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.
Reinforcement Learning 5. Monte Carlo MethodsSeung Jae Lee
A summary of Chapter 5: Monte Carlo Methods of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
Check my website for more slides of books and papers!
https://www.endtoend.ai
Topological Data Analysis: visual presentation of multidimensional data setsDataRefiner
Topology data analysis (TDA) is an unsupervised approach which may revolutionise the way data can be mined and eventually drive the new generation of analytical tools. The idea behind TDA is an attempt to "measure" shape of data and find compressed combinatorial representation of the shape. In ordinary topology, the combinatorial representations serve the purpose of providing the compressed representation of high dimensional data sets which retains information about the geometric relationships between data points. TDA can also be used as a very powerful clustering technique. Edward will present the comparison between TDA and other dimension reduction algorithms like PCA, LLE, Isomap, MDS, and Spectral Embedding.
An Neural Network (NN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.
It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.
An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.
An artificial neuron is a device with many inputs and one output. The neuron has two modes of operation; the training mode and the using mode. In the training mode, the neuron can be trained to fire (or not), for particular input patterns.
In the using mode, when a taught input pattern is detected at the input, its associated output becomes the current output.
Real Time Object Detection System with YOLO and CNN Models: A ReviewSpringer
The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK
ONCE (YOLO) algorithm and it's more evolved versions are briefly described in this research survey. This
survey is all about YOLO and convolution neural networks (CNN) in the direction of real time object detection.
YOLO does generalized object representation more effectively without precision losses than other object
detection models. CNN architecture models have the ability to eliminate highlights and identify objects in any
given image. When implemented appropriately, CNN models can address issues like deformity diagnosis,
creating educational or instructive application, etc. This article reached at number of observations and
perspective findings through the analysis. Also it provides support for the focused visual information and
feature extraction in the financial and other industries, highlights the method of target detection and feature
selection, and briefly describes the development process of yolo algorithm
Survey for recursive neural networks. Including recursive neural network (RNN), recursive autoencoder (RAE), unfolding RAE & dynamic pooling, matrix-vector RNN (MV-RNN), and recursive neural tensor network (RNTN), published by Socher et al.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Unsupervised generative methods have undergone a recent renaissance, spurred on in large part by impressive photo-realistic results in image applications. These generative methods seek to yield models that understand data by learning how to generate samples through implicit and explicit likelihood optimization. However, despite the surge in interest, these models are limited in several key aspects. First, although methods with an explicit likelihood are, in principle, able to perform additional tasks like anomaly detection and imputation, biases in the learned likelihood render these models useless for such important tasks. For example, recent work has shown that modern methods lead to high out-of-distribution likelihoods for data that is unlike seen training instances. Secondly, most current generative methods are limited to fixed-length vector or sequential data, leaving a substantial gap for the analysis of exchangeable data like sets and graphs. I.e., modern generative models excel at modeling dependencies among features in a point, but are lacking in modeling dependencies amongpoints in a collection. In this talk I discuss these shortcomings and suggest some possible avenues for improvement.
A New Model for Credit Approval Problems: A Neuro-Genetic System with Quantum...Anderson Pinho
This paper presents a new model for neuro-evolutionary systems. It is a new quantum-inspired evolutionary algorithm with binary-real representation (QIEA-BR) for evolution of a neural network. The proposed model is an extension of the QIEA-R developed for numerical optimization. The Quantum-Inspired Neuro-Evolutionary Computation model (QINEA-BR) is able to completely configure a feed-forward neural network in terms of selecting the relevant input variables, number of neurons in the hidden layer and all existent synaptic weights. QINEA-BR is evaluated in a benchmark problem of financial credit evaluation. The results obtained demonstrate the effectiveness of this new model in comparison with other machine learning and statistical models, providing good accuracy in separating good from bad customers.
Ordinal Regression and Machine Learning: Applications, Methods, MetricsFrancesco Casalegno
What do movie recommender systems, disease progression evaluation, and sovereign credit ranking have in common?
→ ordinal regression sits between classification and regression
→ target values are categorical and discrete, but ordered
→ many challenges to face when training and evaluating models
What will you find in this presentation?
→ real life, clear examples of ordinal regression you see everyday
→ learning to rank: predict user preferences and items relevance
→ best solution methods: naïve, binary decomposition, threshold
→ how to measure performance: understand & choose metrics
Similar to Estimation of Distribution Algorithms Tutorial (7)
Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOAMartin Pelikan
An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distance-based statistics. The technique enables practitioners to improve hBOA efficiency by collecting statistics from probabilistic models obtained in previous hBOA runs and using the obtained statistics to bias future hBOA runs on similar problems. The purpose of this paper is threefold: (1) test the technique on several classes of NP-complete problems, including MAXSAT, spin glasses and minimum vertex cover; (2) demonstrate that the technique is effective even when previous runs were done on problems of different size; (3) provide empirical evidence that combining transfer learning with other efficiency enhancement techniques can often yield nearly multiplicative speedups.
Population Dynamics in Conway’s Game of Life and its VariantsMartin Pelikan
The presentation for the project of high school students Yonatan Biel and David Hua made in the Students and Teachers As Research Scientists (STARS) program at the Missouri Estimation of Distribution Algorithms Laboratory (MEDAL). To see animations, please download the powerpoint presentation.
Image segmentation using a genetic algorithm and hierarchical local searchMartin Pelikan
This paper proposes a hybrid genetic algorithm to perform image segmentation based on applying the q-state Potts spin glass model to a grayscale image. First, the image is converted to a set of weights for a q-state spin glass and then a steady-state genetic algorithm is used to evolve candidate segmented images until a suitable candidate solution is found. To speed up the convergence to an adequate solution, hierarchical local search is used on each evaluated solution. The results show that the hybrid genetic algorithm with hierarchical local search is able to efficiently perform image segmentation. The necessity of hierarchical search for these types of problems is also clearly demonstrated.
Distance-based bias in model-directed optimization of additively decomposable...Martin Pelikan
For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies between variables that are closer to each other with respect to the metric are expected to be stronger than the dependencies between variables that are further apart. The purpose of this paper is to describe a method that combines such a problem-specific distance metric with information mined from probabilistic models obtained in previous runs of estimation of distribution algorithms with the goal of solving future problem instances of similar type with increased speed, accuracy and reliability. While the focus of the paper is on additively decomposable problems and the hierarchical Bayesian optimization algorithm, it should be straightforward to generalize the approach to other model-directed optimization techniques and other problem classes. Compared to other techniques for learning from experience put forward in the past, the proposed technique is both more practical and more broadly applicable.
Pairwise and Problem-Specific Distance Metrics in the Linkage Tree Genetic Al...Martin Pelikan
The linkage tree genetic algorithm (LTGA) identifies linkages between problem variables using an agglomerative hierarchical clustering algorithm and linkage trees. This enables LTGA to solve many decomposable problems that are difficult with more conventional genetic algorithms. The goal of this paper is two-fold: (1) Present a thorough empirical evaluation of LTGA on a large set of problem instances of additively decomposable problems and (2) speed up the clustering algorithm used to build the linkage trees in LTGA by using a pairwise and a problem-specific metric.
http://medal.cs.umsl.edu/files/2011001.pdf
Effects of a Deterministic Hill climber on hBOAMartin Pelikan
Hybridization of global and local search algorithms is a well-established technique for enhancing the efficiency of search algorithms. Hybridizing estimation of distribution algorithms (EDAs) has been repeatedly shown to produce better performance than either the global or local search algorithm alone. The hierarchical Bayesian optimization algorithm (hBOA) is an advanced EDA which has previously been shown to benefit from hybridization with a local searcher. This paper examines the effects of combining hBOA with a deterministic hill climber (DHC). Experiments reveal that allowing DHC to find the local optima makes model building and decision making much easier for hBOA. This reduces the minimum population size required to find the global optimum, which substantially improves overall performance.
Intelligent Bias of Network Structures in the Hierarchical BOAMartin Pelikan
One of the primary advantages of estimation of distribution algorithms (EDAs) over many other stochastic optimization techniques is that they supply us with a roadmap of how they solve a problem. This roadmap consists of a sequence of probabilistic models of candidate solutions of increasing quality. The first model in this sequence would typically encode the uniform distribution over all admissible solutions whereas the last model would encode a distribution that generates at least one global optimum with high probability. It has been argued that exploiting this knowledge should improve EDA performance when solving similar problems. This paper presents an approach to bias the building of Bayesian network models in the hierarchical Bayesian optimization algorithm (hBOA) using information gathered from models generated during previous hBOA runs on similar problems. The approach is evaluated on trap-5 and 2D spin glass problems.
Using Previous Models to Bias Structural Learning in the Hierarchical BOAMartin Pelikan
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutionary algorithms, this readily available source of problem-specific information has been practically ignored by the EDA community. This paper takes the first step towards the use of probabilistic models obtained by EDAs to speed up the solution of similar problems in future. More specifically, we propose two approaches to biasing model building in the hierarchical Bayesian optimization algorithm (hBOA) based on knowledge automatically learned from previous hBOA runs on similar problems. We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.
Finding Ground States of Sherrington-Kirkpatrick Spin Glasses with Hierarchic...Martin Pelikan
This study focuses on the problem of finding ground states of random instances of the Sherrington-Kirkpatrick (SK) spin-glass model with Gaussian couplings. While the ground states of SK spin-glass instances can be obtained with branch and bound, the computational complexity of branch and bound yields instances of not more than about 90 spins. We describe several approaches based on the hierarchical Bayesian optimization algorithm (hBOA) to reliably identifying ground states of SK instances intractable with branch and bound, and present a broad range of empirical results on such problem instances. We argue that the proposed methodology holds a big promise for reliably solving large SK spin-glass instances to optimality with practical time complexity. The proposed approaches to identifying global optima reliably can also be applied to other problems and they can be used with many other evolutionary algorithms. Performance of hBOA is compared to that of the genetic algorithm with two common crossover operators.
iBOA: The Incremental Bayesian Optimization AlgorithmMartin Pelikan
This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.
Fitness inheritance in the Bayesian optimization algorithmMartin Pelikan
This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems where fitness evaluation is expensive. Bayesian networks used in BOA to model promising solutions and generate the new ones are extended to allow not only for modeling and sampling candidate solutions, but also for estimating their fitness. The results indicate that fitness inheritance is a promising concept in BOA, because population-sizing requirements for building appropriate models of promising solutions lead to good fitness estimates even if only a small proportion of candidate solutions is evaluated using the actual fitness function. This can lead to a reduction of the number of actual fitness evaluations by a factor of 30 or more.
Computational complexity and simulation of rare events of Ising spin glasses Martin Pelikan
We discuss the computational complexity of random 2D Ising spin glasses, which represent an interesting class of constraint satisfaction problems for black box optimization. Two extremal cases are considered: (1) the +/- J spin glass, and (2) the Gaussian spin glass. We also study a smooth transition between these two extremal cases. The computational complexity of all studied spin glass systems is found to be dominated by rare events of extremely hard spin glass samples. We show that complexity of all studied spin glass systems is closely related to Frechet extremal value distribution. In a hybrid algorithm that combines the hierarchical Bayesian optimization algorithm (hBOA) with a deterministic bit-flip hill climber, the number of steps performed by both the global searcher (hBOA) and the local searcher follow Frechet distributions. Nonetheless, unlike in methods based purely on local search, the parameters of these distributions confirm good scalability of hBOA with local search. We further argue that standard performance measures for optimization algorithms---such as the average number of evaluations until convergence---can be misleading. Finally, our results indicate that for highly multimodal constraint satisfaction problems, such as Ising spin glasses, recombination-based search can provide qualitatively better results than mutation-based search.
The Bayesian Optimization Algorithm with Substructural Local SearchMartin Pelikan
This work studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.
Analyzing Probabilistic Models in Hierarchical BOA on Traps and Spin GlassesMartin Pelikan
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.
Hybrid Evolutionary Algorithms on Minimum Vertex Cover for Random GraphsMartin Pelikan
This work analyzes the hierarchical Bayesian optimization algorithm (hBOA) on minimum vertex cover for standard classes of random graphs and transformed SAT instances. The performance of hBOA is compared with that of the branch-and-bound problem solver (BB), the simple genetic algorithm (GA) and the parallel simulated annealing (PSA). The results indicate that BB is significantly outperformed by all the other tested methods, which is expected as BB is a complete search algorithm and minimum vertex cover is an NP-complete problem. The best performance is achieved by hBOA; nonetheless, the performance differences between hBOA and other evolutionary algorithms are relatively small, indicating that mutation-based search and recombination-based search lead to similar performance on the tested classes of minimum vertex cover problems.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Leading Change strategies and insights for effective change management pdf 1.pdf
Estimation of Distribution Algorithms Tutorial
1. Probabilistic Model-Building
Genetic Algorithms
a.k.a. Estimation of Distribution Algorithms
a.k.a. Iterated Density Estimation Algorithms
Martin Pelikan
Missouri Estimation of Distribution Algorithms Laboratory (MEDAL)
Department of Mathematics and Computer Science
University of Missouri - St. Louis
martin@martinpelikan.net
Copyright is held by the author/owner(s).
http://martinpelikan.net/
GECCO’12 Companion, July 7–11, 2012, Philadelphia, PA, USA.
ACM 978-1-4503-1178-6/12/07.
2. Foreword
n Motivation
¨ Genetic and evolutionary computation (GEC) popular.
¨ Toy problems great, but difficulties in practice.
¨ Must design new representations, operators, tune, …
n This talk
¨ Discuss a promising direction in GEC.
¨ Combine machine learning and GEC.
¨ Create practical and powerful optimizers.
Martin Pelikan, Probabilistic Model-Building GAs
2
3. Overview
n Introduction
¨ Black-box optimization via probabilistic modeling.
n Probabilistic Model-Building GAs
¨ Discreterepresentation
¨ Continuous representation
¨ Computer programs (PMBGP)
¨ Permutations
n Conclusions
Martin Pelikan, Probabilistic Model-Building GAs
3
4. Problem Formulation
n Input
¨ How do potential solutions look like?
¨ How to evaluate quality of potential solutions?
n Output
¨ Best solution (the optimum).
n Important
¨ No additional knowledge about the problem.
Martin Pelikan, Probabilistic Model-Building GAs
4
5. Why View Problem as Black Box?
n Advantages
¨ Separate problem definition from optimizer.
¨ Easy to solve new problems.
¨ Economy argument.
n Difficulties
¨ Almost no prior problem knowledge.
¨ Problem specifics must be learned automatically.
¨ Noise, multiple objectives, interactive evaluation.
Martin Pelikan, Probabilistic Model-Building GAs
5
6. Representations Considered Here
n Start with
¨ Solutions are n-bit binary strings.
n Later
¨ Real-valuedvectors.
¨ Program trees.
¨ Permutations
Martin Pelikan, Probabilistic Model-Building GAs
6
7. Typical Situation
n Previously visited solutions + their evaluation:
#
Solution Evaluation
1
00100
1
2
11011
4
3
01101
0
4
10111
3
n Question: What solution to generate next?
Martin Pelikan, Probabilistic Model-Building GAs
7
8. Many Answers
n Hill climber
¨ Start with a random solution.
¨ Flip bit that improves the solution most.
¨ Finish when no more improvement possible.
n Simulated annealing
¨ Introduce Metropolis.
n Probabilistic model-building GAs
¨ Inspiration from GAs and machine learning (ML).
Martin Pelikan, Probabilistic Model-Building GAs
8
9. Probabilistic Model-Building GAs
Current Selected New
population population population
11001
11001
01111
11101
10101
Probabilistic 11001
01011
01011
Model 11011
11000
11000
00111
…replace crossover+mutation with learning
and sampling probabilistic model
Martin Pelikan, Probabilistic Model-Building GAs
9
10. Other Names for PMBGAs
n Estimation of distribution algorithms (EDAs)
(Mühlenbein & Paass, 1996)
n Iterated density estimation algorithms (IDEA)
(Bosman & Thierens, 2000)
Martin Pelikan, Probabilistic Model-Building GAs
10
11. Implicit vs. Explicit Model
n GAs and PMBGAs perform similar task
¨ Generate new solutions using probability
distribution based on selected solutions.
n GAs
¨ Variationdefines implicit probability distribution of
target population given original population and
variation operators (crossover and mutation).
n PMBGAs
¨ Explicit
probabilistic model of selected candidate
solutions is built and sampled.
Martin Pelikan, Probabilistic Model-Building GAs
11
12. What Models to Use?
n Start with a simple example
¨ Probability vector for binary strings.
n Later
¨ Dependency tree models (COMIT).
¨ Bayesian networks (BOA).
¨ Bayesian networks with local structures (hBOA).
Martin Pelikan, Probabilistic Model-Building GAs
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13. Probability Vector
n Assume n-bit binary strings.
n Model: Probability vector p=(p1, …, pn)
¨ pi= probability of 1 in position i
¨ Learn p: Compute proportion of 1 in each position.
¨ Sample p: Sample 1 in position i with prob. pi
Martin Pelikan, Probabilistic Model-Building GAs
13
14. Example: Probability Vector
(Mühlenbein, Paass, 1996), (Baluja, 1994)
Current Selected New
population population population
Probability
11001 11001 vector 10101
10101 10101 10001
1.0 0.5 0.5 0.0 1.0
01011 01011 11101
11000 11000 11001
Martin Pelikan, Probabilistic Model-Building GAs
14
15. Probability Vector PMBGAs
n PBIL (Baluja, 1995)
¨ Incremental updates to the prob. vector.
n Compact GA (Harik, Lobo, Goldberg, 1998)
¨ Also
incremental updates but better analogy with
populations.
n UMDA (Mühlenbein, Paass, 1996)
¨ What we showed here.
n DEUM (Shakya et al., 2004)
n All variants perform similarly.
Martin Pelikan, Probabilistic Model-Building GAs
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16. Probability Vector Dynamics
n Bitsthat perform better get more copies.
n And are combined in new ways.
n But context of each bit is ignored.
n Example problem 1: Onemax
n
f ( X 1 , X 2 ,… , X n ) = ∑ X i
i =1
Martin Pelikan, Probabilistic Model-Building GAs
16
17. Probability Vector on Onemax
1
0.9
Probability vector entries
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0 10 20 30 40 50
Generation
Martin Pelikan, Probabilistic Model-Building GAs
17
18. Probability Vector: Ideal Scale-up
n O(n log n) evaluations until convergence
¨ (Harik,
Cantú-Paz, Goldberg, & Miller, 1997)
¨ (Mühlenbein, Schlierkamp-Vosen, 1993)
n Other algorithms
¨ Hill
climber: O(n log n) (Mühlenbein, 1992)
¨ GA with uniform: approx. O(n log n)
¨ GA with one-point: slightly slower
Martin Pelikan, Probabilistic Model-Building GAs
18
19. When Does Prob. Vector Fail?
n Example problem 2: Concatenated traps
¨ Partition
input string into disjoint groups of 5 bits.
¨ Groups contribute via trap (ones=number of ones):
" 5 if ones = 5
trap ( ones ) = #
$ 4 − ones otherwise
¨ Concatenated trap = sum of single traps
¨ Optimum: String 111…1
Martin Pelikan, Probabilistic Model-Building GAs
19
20. Trap-5
5
4
trap(u)
3
2
1
0
0 1 2 3 4 5
Number of ones, u
Martin Pelikan, Probabilistic Model-Building GAs
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21. Probability Vector on Traps
1
0.9
Probability vector entries
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0 10 20 30 40 50
Generation
Martin Pelikan, Probabilistic Model-Building GAs
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22. Why Failure?
n Onemax:
¨ Optimum in 111…1
¨ 1 outperforms 0 on average.
n Traps: optimum in 11111, but
n f(0****) = 2
n f(1****) = 1.375
n So single bits are misleading.
Martin Pelikan, Probabilistic Model-Building GAs
22
23. How to Fix It?
n Consider 5-bit statistics instead 1-bit ones.
n Then, 11111 would outperform 00000.
n Learn model
¨ Compute p(00000), p(00001), …, p(11111)
n Sample model
¨ Sample 5 bits at a time
¨ Generate 00000 with p(00000),
00001 with p(00001), …
Martin Pelikan, Probabilistic Model-Building GAs
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24. Correct Model on Traps: Dynamics
1
0.9
Probabilities of 11111
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0 10 20 30 40 50
Generation 24
Martin Pelikan, Probabilistic Model-Building GAs
25. Good News: Good Stats Work Great!
n Optimum in O(n log n) evaluations.
n Same performance as on onemax!
n Others
¨ Hill
climber: O(n5 log n) = much worse.
¨ GA with uniform: O(2n) = intractable.
¨ GA with k-point xover: O(2n) (w/o tight linkage).
Martin Pelikan, Probabilistic Model-Building GAs
25
26. Challenge
n If we could learn and use relevant context for
each position
¨ Find non-misleading statistics.
¨ Use those statistics as in probability vector.
n Then we could solve problems decomposable
into statistics of order at most k with at most
O(n2) evaluations!
¨ And there are many such problems (Simon, 1968).
Martin Pelikan, Probabilistic Model-Building GAs
26
27. What’s Next?
n COMIT
¨ Use tree models
n Extended compact GA
¨ Cluster bits into groups.
n Bayesian optimization algorithm (BOA)
¨ Use Bayesian networks (more general).
Martin Pelikan, Probabilistic Model-Building GAs
27
28. Beyond single bits: COMIT
(Baluja, Davies, 1997)
Model P(X=1)
75 %
String
X P(Y=1|X)
0
30 %
1
25 %
X P(Z=1|X)
0
86 %
1 75 %
Martin Pelikan, Probabilistic Model-Building GAs
28
29. How to Learn a Tree Model?
n Mutual information:
P(Xi = a, X j = b)
I(Xi , X j ) = ∑ P(Xi = a, X j = b) log
a,b P(Xi = a)P(X j = b)
n Goal
¨ Find tree that maximizes mutual information
between connected nodes.
¨ Will minimize Kullback-Leibler divergence.
n Algorithm
¨ Prim’s algorithm for maximum spanning trees.
Martin Pelikan, Probabilistic Model-Building GAs
29
30. Prim’s Algorithm
n Start with a graph with no edges.
n Add arbitrary node to the tree.
n Iterate
¨ Hang a new node to the current tree.
¨ Prefer addition of edges with large mutual
information (greedy approach).
n Complexity: O(n2)
Martin Pelikan, Probabilistic Model-Building GAs
30
31. Variants of PMBGAs with Tree Models
n COMIT (Baluja, Davies, 1997)
¨ Tree models.
n MIMIC (DeBonet, 1996)
¨ Chain distributions.
n BMDA (Pelikan, Mühlenbein, 1998)
¨ Forest distribution (independent trees or tree)
Martin Pelikan, Probabilistic Model-Building GAs
31
32. Beyond Pairwise Dependencies: ECGA
n Extended Compact GA (ECGA) (Harik, 1999).
n Consider groups of string positions.
String Model
00
16 %
0
86 %
000
17 %
01
45 %
1
14 %
001
2 %
10
35 %
···
11
4 %
111
24 %
Martin Pelikan, Probabilistic Model-Building GAs
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33. Learning the Model in ECGA
n Start with each bit in a separate group.
n Each iteration merges two groups for best
improvement.
Martin Pelikan, Probabilistic Model-Building GAs
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34. How to Compute Model Quality?
n ECGA uses minimum description length.
n Minimize number of bits to store model+data:
MDL( M , D) = DModel + DData
n Each frequency needs (0.5 log N) bits:
|g|−1
DModel = ∑ 2 log N
g ∈G
n Each solution X needs -log p(X) bits:
DData = − N ∑ p( X ) log p( X )
X
Martin Pelikan, Probabilistic Model-Building GAs
34
35. Sampling Model in ECGA
n Sample groups of bits at a time.
n Based on observed probabilities/proportions.
n But can also apply population-based crossover
similar to uniform but w.r.t. model.
Martin Pelikan, Probabilistic Model-Building GAs
35
36. Building-Block-Wise Mutation in ECGA
n Sastry, Goldberg (2004); Lima et al. (2005)
n Basic idea
¨ Use ECGA model builder to identify decomposition
¨ Use the best solution for BB-wise mutation
¨ For each k-bit partition (building block)
n Evaluate the remaining 2k-1 instantiations of this BB
n Use the best instantiation of this BB
n Result (for order-k separable problems)
¨ ( )
BB-wise mutation is O k log n times faster than ECGA!
¨ But only for separable problems (and similar ones).
Martin Pelikan, Probabilistic Model-Building GAs
36
37. What’s Next?
n We saw
¨ Probabilityvector (no edges).
¨ Tree models (some edges).
¨ Marginal product models (groups of variables).
n Next: Bayesian networks
¨ Can represent all above and more.
Martin Pelikan, Probabilistic Model-Building GAs
37
38. Bayesian Optimization Algorithm (BOA)
n Pelikan, Goldberg, & Cantú-Paz (1998)
n Use a Bayesian network (BN) as a model.
n Bayesian network
¨ Acyclicdirected graph.
¨ Nodes are variables (string positions).
¨ Conditional dependencies (edges).
¨ Conditional independencies (implicit).
Martin Pelikan, Probabilistic Model-Building GAs
38
39. Example: Bayesian Network (BN)
n Conditional dependencies.
n Conditional independencies.
Martin Pelikan, Probabilistic Model-Building GAs
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40. BOA
Bayesian
Current Selected New
network
population population population
Martin Pelikan, Probabilistic Model-Building GAs
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41. Learning BNs
n Two things again:
¨ Scoringmetric (as MDL in ECGA).
¨ Search procedure (in ECGA done by merging).
Martin Pelikan, Probabilistic Model-Building GAs
41
42. Learning BNs: Scoring Metrics
n Bayesian metrics
¨ Bayesian-Dirichlet with likelihood equivallence
n
Γ(m'(π i )) Γ(m'(xi , π i ) + m(xi , π i ))
BD(B) = p(B)∏ ∏ ∏ Γ(m'(x ,π ))
i=1 π Γ(m'(π i ) + m(π i )) xi i i
i
n Minimum description length metrics
¨ Bayesian information criterion (BIC)
Πi log 2 N &
n#
BIC( B) = ∑ % − H ( X i | Πi )N − 2
i=1 $ 2 ( '
Martin Pelikan, Probabilistic Model-Building GAs
42
43. Learning BNs: Search Procedure
n Start with empty network (like ECGA).
n Execute primitive operator that improves the
metric the most (greedy).
n Until no more improvement possible.
n Primitive operators
¨ Edge addition (most important).
¨ Edge removal.
¨ Edge reversal.
Martin Pelikan, Probabilistic Model-Building GAs
43
45. BOA and Problem Decomposition
n Conditions for factoring problem decomposition
into a product of prior and conditional
probabilities of small order in Mühlenbein,
Mahnig, & Rodriguez (1999).
n In practice, approximate factorization sufficient
that can be learned automatically.
n Learning makes complete theory intractable.
Martin Pelikan, Probabilistic Model-Building GAs
45
46. BOA Theory: Population Sizing
n Initial supply (Goldberg et al., 2001)
¨ Have enough stuff to combine. O 2k( )
n Decision making (Harik et al, 1997)
¨ Decide well between competing partial sols. O ( n log n )
n Drift (Thierens, Goldberg, Pereira, 1998)
¨ Don’t lose less salient stuff prematurely. O n ()
n Model building (Pelikan et al., 2000, 2002)
¨ Find a good model. ( )
O n1.05
Martin Pelikan, Probabilistic Model-Building GAs
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47. BOA Theory: Num. of Generations
n Two extreme cases, everything in the middle.
n Uniform scaling
¨ Onemax model (Muehlenbein & Schlierkamp-Voosen, 1993)
O ( n)
n Exponential scaling
¨ Domino convergence (Thierens, Goldberg, Pereira, 1998)
O (n)
Martin Pelikan, Probabilistic Model-Building GAs
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48. Good News: Challenge Met!
n Theory
¨ Population sizing (Pelikan et al., 2000, 2002)
n Initial supply.
n Decision making. O(n) to O(n1.05)
n Drift.
n Model building.
¨ Number of iterations (Pelikan et al., 2000, 2002)
n Uniform scaling.
n Exponential scaling. O(n0.5) to O(n)
n BOA solves order-k decomposable problems in O(n1.55) to
O(n2) evaluations!
Martin Pelikan, Probabilistic Model-Building GAs
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49. Theory vs. Experiment (5-bit Traps)
500000
450000
Experiment
400000
Theory
350000
Number of Evaluations
300000
250000
200000
150000
100000
100
125
150
175
200
225
250
Problem Size 49
Martin Pelikan, Probabilistic Model-Building GAs
50. BOA Siblings
n Estimation of Bayesian Networks Algorithm
(EBNA) (Etxeberria, Larrañaga, 1999).
n Learning Factorized Distribution Algorithm
(LFDA) (Mühlenbein, Mahnig, Rodriguez, 1999).
Martin Pelikan, Probabilistic Model-Building GAs
50
51. Another Option: Markov Networks
n MN-FDA, MN-EDA (Santana; 2003, 2005)
n Similar to Bayes nets but with undirected edges.
Martin Pelikan, Probabilistic Model-Building GAs
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52. Yet Another Option: Dependency Networks
n Estimation of dependency networks algorithm (EDNA)
¨ Gamez, Mateo, Puerta (2007).
¨ Use dependency network as a model.
¨ Dependency network learned from pairwise interactions.
¨ Use Gibbs sampling to generate new solutions.
n Dependency network
¨ Parents of a variable= all variables influencing this variable.
¨ Dependency network can contain cycles.
Martin Pelikan, Probabilistic Model-Building GAs
52
53. Model Comparison
BMDA ECGA BOA
Model Expressiveness Increases
Martin Pelikan, Probabilistic Model-Building GAs
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54. From single level to hierarchy
n Single-level decomposition powerful.
n But what if single-level decomposition is not
enough?
n Learn from humans and nature
¨ Decompose problem over multiple levels.
¨ Use solutions from lower level as basic building
blocks.
¨ Solve problem hierarchically.
Martin Pelikan, Probabilistic Model-Building GAs
54
55. Hierarchical Decomposition
Car
Engine Braking system Electrical system
Fuel system Valves Ignition system
Martin Pelikan, Probabilistic Model-Building GAs
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56. Three Keys to Hierarchy Success
n Proper decomposition
¨ Must decompose problem on each level properly.
n Chunking
¨ Must represent & manipulate large order solutions.
n Preservation of alternative solutions
¨ Must
preserve alternative partial solutions
(chunks).
Martin Pelikan, Probabilistic Model-Building GAs
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57. Hierarchical BOA (hBOA)
n Pelikan & Goldberg (2000, 2001)
n Proper decomposition
¨ Use Bayesian networks like BOA.
n Chunking
¨ Use local structures in Bayesian networks.
n Preservation of alternative solutions.
¨ Use restricted tournament replacement (RTR).
¨ Can use other niching methods.
Martin Pelikan, Probabilistic Model-Building GAs
57
58. Local Structures in BNs
n Look at one conditional dependency.
¨ 2k probabilities for k parents.
n Why not use more powerful representations
for conditional probabilities?
X2X3 P(X1=0|X2X3)
X1
00
26 %
01
44 %
X2 X3
10
15 %
11
15 %
Martin Pelikan, Probabilistic Model-Building GAs
58
59. Local Structures in BNs
n Look at one conditional dependency.
¨ 2k probabilities for k parents.
n Why not use more powerful representations
for conditional probabilities?
X2
X1 0
1
X3 15%
X2 X3
0
1
26%
44%
Martin Pelikan, Probabilistic Model-Building GAs
59
60. Restricted Tournament Replacement
n Used in hBOA for niching.
n Insert each new candidate solution x like this:
¨ Pick random subset of original population.
¨ Find solution y most similar to x in the subset.
¨ Replace y by x if x is better than y.
Martin Pelikan, Probabilistic Model-Building GAs
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61. Hierarchical Traps: The Ultimate Test
n Combine traps on more levels.
n Each level contributes to fitness.
n Groups of bits map to next level.
Martin Pelikan, Probabilistic Model-Building GAs
61
62. hBOA on Hierarchical Traps
Experiment
6
10 O(n1.63 log(n))
Number of Evaluations
5
10
4
10
27 81 243 729
Problem Size
Martin Pelikan, Probabilistic Model-Building GAs
62
63. PMBGAs Are Not Just Optimizers
n PMBGAs provide us with two things
¨ Optimum or its approximation.
¨ Sequence of probabilistic models.
n Probabilistic models
¨ Encode populations of increasing quality.
¨ Tell us a lot about the problem at hand.
¨ Can we use this information?
Martin Pelikan, Probabilistic Model-Building GAs
63
64. Efficiency Enhancement for PMBGAs
n Sometimes O(n2) is not enough
¨ High-dimensional problems (1000s of variables)
¨ Expensive evaluation (fitness) function
n Solution
¨ Efficiency enhancement techniques
Martin Pelikan, Probabilistic Model-Building GAs
64
65. Efficiency Enhancement Types
n 7 efficiency enhancement types for PMBGAs
¨ Parallelization
¨ Hybridization
¨ Time continuation
¨ Fitness evaluation relaxation
¨ Prior knowledge utilization
¨ Incremental and sporadic model building
¨ Learning from experience
Martin Pelikan, Probabilistic Model-Building GAs
65
66. Multi-objective PMBGAs
n Methods for multi-objective GAs adopted
¨ Multi-objective mixture-based IDEAs
(Thierens, & Bosman, 2001)
¨ Another multi-objective BOA (from SPEA2 and mBOA)
(Laumanns, & Ocenasek, 2002)
¨ Multi-objective hBOA (from NSGA-II and hBOA)
(Khan, Goldberg, & Pelikan, 2002)
(Pelikan, Sastry, & Goldberg, 2005)
¨ Regularity Model Based Multiobjective EDA (RM-MEDA)
(Zhang, Zhou, Jin, 2008)
Martin Pelikan, Probabilistic Model-Building GAs
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67. Promising Results with Discrete PMBGAs
n Artificial classes of problems
n Physics
n Bioinformatics
n Computational complexity and AI
n Others
Martin Pelikan, Probabilistic Model-Building GAs
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68. Results: Artificial Problems
n Decomposition
¨ Concatenated traps (Pelikan et al., 1998).
n Hierarchical decomposition
¨ Hierarchical traps (Pelikan, Goldberg, 2001).
n Other sources of difficulty
¨ Exponential scaling, noise (Pelikan, 2002).
Martin Pelikan, Probabilistic Model-Building GAs
68
69. BOA on Concatenated 5-bit Traps
500000
450000
Experiment
400000
Theory
350000
Number of Evaluations
300000
250000
200000
150000
100000
100
125
150
175
200
225
250
Problem Size
Martin Pelikan, Probabilistic Model-Building GAs
69
70. hBOA on Hierarchical Traps
Experiment
6
10 O(n1.63 log(n))
Number of Evaluations
5
10
4
10
27 81 243 729
Problem Size
Martin Pelikan, Probabilistic Model-Building GAs
70
71. Results: Physics
n Spin glasses (Pelikan et al., 2002, 2006, 2008)
(Hoens, 2005) (Santana, 2005) (Shakya et al.,
2006)
¨ ±J and Gaussian couplings
¨ 2D and 3D spin glass
¨ Sherrington-Kirkpatrick (SK) spin glass
n Silicon clusters (Sastry, 2001)
¨ Gong potential (3-body)
Martin Pelikan, Probabilistic Model-Building GAs
71
72. hBOA on Ising Spin Glasses (2D)
hBOA
O(n1.51)
Number ofof Evaluations
Number Evaluations
3
10
64 100 144 196 256 324 400
Problem Size
Number of Spins
Martin Pelikan, Probabilistic Model-Building GAs
72
73. Results on 2D Spin Glasses
n Number of evaluations is O(n 1.51).
n Overall time is O(n 3.51).
n Compare O(n3.51) to O(n3.5) for best method
(Galluccio & Loebl, 1999)
n Great also on Gaussians.
Martin Pelikan, Probabilistic Model-Building GAs
73
74. hBOA on Ising Spin Glasses (3D)
6
10
Experimental average
O(n3.63 )
Number of Evaluations
Number of Evaluations
5
10
4
10
3
10
64 125 216 343
Problem Size
Number of Spins
Martin Pelikan, Probabilistic Model-Building GAs
74
75. hBOA on SK Spin Glass
Martin Pelikan, Probabilistic Model-Building GAs
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76. Results: Computational Complexity, AI
n MAXSAT, SAT (Pelikan, 2002)
¨ Random 3CNF from phase transition.
¨ Morphed graph coloring.
¨ Conversion from spin glass.
n Feature subset selection (Inza et al., 2001)
(Cantu-Paz, 2004)
Martin Pelikan, Probabilistic Model-Building GAs
76
77. Results: Some Others
n Military antenna design (Santarelli et al., 2004)
n Groundwater remediation design (Arst et al., 2004)
n Forest management (Ducheyne et al., 2003)
n Nurse scheduling (Li, Aickelin, 2004)
n Telecommunication network design (Rothlauf, 2002)
n Graph partitioning (Ocenasek, Schwarz, 1999; Muehlenbein, Mahnig,
2002; Baluja, 2004)
n Portfolio management (Lipinski, 2005, 2007)
n Quantum excitation chemistry (Sastry et al., 2005)
n Maximum clique (Zhang et al., 2005)
n Cancer chemotherapy optimization (Petrovski et al., 2006)
n Minimum vertex cover (Pelikan et al., 2007)
n Protein folding (Santana et al., 2007)
n Side chain placement (Santana et al., 2007)
Martin Pelikan, Probabilistic Model-Building GAs
77
78. Discrete PMBGAs: Summary
n No interactions
¨ Univariate models; PBIL, UMDA, cGA.
n Some pairwise interactions
¨ Tree models; COMIT, MIMIC, BMDA.
n Multivariate interactions
¨ Multivariate models: BOA, EBNA, LFDA.
n Hierarchical decomposition
¨ hBOA
Martin Pelikan, Probabilistic Model-Building GAs
78
79. Discrete PMBGAs: Recommendations
n Easy problems
¨ Use univariate models; PBIL, UMDA, cGA.
n Somewhat difficult problems
¨ Use bivariate models; MIMIC, COMIT, BMDA.
n Difficult problems
¨ Use multivariate models; BOA, EBNA, LFDA.
n Most difficult problems
¨ Use hierarchical decomposition; hBOA.
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80. Real-Valued PMBGAs
n New challenge
¨ Infinite
domain for each variable.
¨ How to model?
n 2 approaches
¨ Discretize
and apply discrete model/PMBGA
¨ Create model for real-valued variables
n Estimate pdf.
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81. PBIL Extensions: First Step
n SHCwL: Stochastic hill climbing with learning
(Rudlof, Köppen, 1996).
n Model
¨ Single-peak Gaussian for each variable.
¨ Means evolve based on parents (promising solutions).
¨ Deviations equal, decreasing over time.
n Problems
¨ No interactions.
¨ Single Gaussians=can model only one attractor.
¨ Same deviations for each variable.
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82. Use Different Deviations
n Sebag, Ducoulombier (1998)
n Some variables have higher variance.
n Use special standard deviation for each
variable.
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83. Use Covariance
n Covariance allows rotation of 1-peak Gaussians.
n EGNA (Larrañaga et al., 2000)
n IDEA (Bosman, Thierens, 2000)
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84. How Many Peaks?
n One Gaussian vs. kernel around each point.
n Kernel distribution similar to ES.
n IDEA (Bosman, Thierens, 2000)
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85. Mixtures: Between One and Many
n Mixture distributions provide transition between one
Gaussian and Gaussian kernels.
n Mixture types 4
¨ Over one variable. 2
n Gallagher, Frean, & Downs (1999).
¨ Over all variables. 0
n Pelikan & Goldberg (2000). -2
n Bosman & Thierens (2000).
¨ Over partitions of variables. -4
-4 -2 0 2 4
n Bosman & Thierens (2000).
n Ahn, Ramakrishna, and Goldberg (2004).
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86. Mixed BOA (mBOA)
n Mixed BOA (Ocenasek, Schwarz, 2002)
n Local distributions
¨ A decision tree (DT) for every variable.
¨ Internal DT nodes encode tests on other variables
n Discrete:Equal to a constant
n Continuous: Less than a constant
¨ Discretevariables:
DT leaves represent probabilities.
¨ Continuous variables:
DT leaves contain a normal kernel distribution.
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87. Real-Coded BOA (rBOA)
n Ahn, Ramakrishna, Goldberg (2003)
n Probabilistic Model
¨ Underlying structure: Bayesian network
¨ Local distributions: Mixtures of Gaussians
n Also extended to multiobjective problems
(Ahn, 2005)
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88. Aggregation Pheromone System (APS)
n Tsutsui (2004)
n Inspired by aggregation pheromones
n Basic idea
¨ Good solutions emit aggregation pheromones
¨ New candidate solutions based on the density of
aggregation pheromones
¨ Aggregation pheromone density encodes a mixture
distribution
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89. Adaptive Variance Scaling
n Adaptive variance in mBOA
¨ Ocenasek et al. (2004)
n Normal IDEAs
¨ Bosman et al. (2006, 2007)
¨ Correlation-triggered adaptive variance scaling
¨ Standard-deviation ratio (SDR) triggered variance
scaling
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90. Real-Valued PMBGAs: Discretization
n Idea: Transform into discrete domain.
n Fixed models
¨ 2k equal-width bins with k-bit binary string.
¨ Goldberg (1989).
¨ Bosman & Thierens (2000); Pelikan et al. (2003).
n Adaptive models
¨ Equal-height histograms of 2k bins.
¨ k-means clustering on each variable.
¨ Pelikan, Goldberg, & Tsutsui (2003); Cantu-Paz (2001).
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91. Real-Valued PMBGAs: Summary
n Discretization
¨ Fixed
¨ Adaptive
n Real-valued models
¨ Single or multiple peaks?
¨ Same variance or different variance?
¨ Covariance or no covariance?
¨ Mixtures?
¨ Treat entire vectors, subsets of variables, or single
variables?
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92. Real-Valued PMBGAs: Recommendations
n Multimodality?
¨ Use multiple peaks.
n Decomposability?
¨ All variables, subsets, or single variables.
n Strong linear dependencies?
¨ Covariance.
n Partial differentiability?
¨ Combine with gradient search.
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93. PMBGP (Genetic Programming)
n New challenge
¨ Structured, variable length representation.
¨ Possibly infinitely many values.
¨ Position independence (or not).
¨ Low correlation between solution quality and
solution structure (Looks, 2006).
n Approaches
¨ Use explicit probabilistic models for trees.
¨ Use models based on grammars.
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94. PIPE
n Probabilistic incremental
program evolution
(Salustowicz & X P(X)
Schmidhuber, 1997) sin 0.15
n Store frequencies of + 0.35
operators/terminals in
- 0.35
nodes of a maximum tree.
X 0.15
n Sampling generates tree
from top to bottom
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95. eCGP
n Sastry & Goldberg (2003)
n ECGA adapted to program trees.
n Maximum tree as in PIPE.
n But nodes partitioned into groups.
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96. BOA for GP
n Looks, Goertzel, & Pennachin (2004)
n Combinatory logic + BOA
¨ Trees translated into uniform structures.
¨ Labels only in leaves.
¨ BOA builds model over symbols in different nodes.
n Complexity build-up
¨ Modeling limited to max. sized structure seen.
¨ Complexity builds up by special operator.
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97. MOSES
n Looks (2006).
n Evolve demes of programs.
n Each deme represents similar structures.
n Apply PMBGA to each deme (e.g. hBOA).
n Introduce new demes/delete old ones.
n Use normal forms to reduce complexity.
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98. PMBGP with Grammars
n Use grammars/stochastic grammars as models.
n Grammars restrict the class of programs.
n Some representatives
¨ Program evolution with explicit learning (Shan et al., 2003)
¨ Grammar-based EDA for GP (Bosman, de Jong, 2004)
¨ Stochastic grammar GP (Tanev, 2004)
¨ Adaptive constrained GP (Janikow, 2004)
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99. PMBGP: Summary
n Interesting starting points available.
n But still lot of work to be done.
n Much to learn from discrete domain, but some
completely new challenges.
n Research in progress
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100. PMBGAs for Permutations
n New challenges
¨ Relativeorder
¨ Absolute order
¨ Permutation constraints
n Two basic approaches
¨ Random-key and real-valued PMBGAs
¨ Explicit probabilistic models for permutations
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101. Random Keys and PMBGAs
n Bengoetxea et al. (2000); Bosman et al. (2001)
n Random keys (Bean, 1997)
¨ Candidate solution = vector of real values
¨ Ascending ordering gives a permutation
n Can use any real-valued PMBGA (or GEA)
¨ IDEAs (Bosman, Thierens, 2002)
¨ EGNA (Larranaga et al., 2001)
n Strengths and weaknesses
¨ Good: Can use any real-valued PMBGA.
¨ Bad: Redundancy of the encoding.
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102. Direct Modeling of Permutations
n Edge-histogram based sampling algorithm
(EHBSA) (Tsutsui, Pelikan, Goldberg, 2003)
¨ Permutations of n elements
¨ Model is a matrix A=(ai,j)i,j=1, 2, …, n
¨ ai,j represents the probability of edge (i, j)
¨ Uses template to reduce exploration
¨ Applicable also to scheduling
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103. ICE: Modify Crossover from Model
n ICE
¨ Bosman, Thierens (2001).
¨ Represent permutations with random keys.
¨ Learn multivariate model to factorize the problem.
¨ Use the learned model to modify crossover.
n Performance
¨ Typically
outperforms IDEAs and other PMBGAs
that learn and sample random keys.
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104. Multivariate Permutation Models
n Basic approach
¨ Use any standard multivariate discrete model.
¨ Restrict sampling to permutations in some way.
¨ Bengoetxea et al. (2000), Pelikan et al. (2007).
n Strengths and weaknesses
¨ Use explicit multivariate models to find regularities.
¨ High-order alphabet requires big samples for good models.
¨ Sampling can introduce unwanted bias.
¨ Inefficient encoding for only relative ordering constraints,
which can be encoded simpler.
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105. Conclusions
n Competent PMBGAs exist
¨ Scalable solution to broad classes of problems.
¨ Solution to previously intractable problems.
¨ Algorithms ready for new applications.
n PMBGAs do more than just solve the problem
¨ They provide us with sequences of probabilistic models.
¨ The probabilistic models tell us a lot about the problem.
n Consequences for practitioners
¨ Robust methods with few or no parameters.
¨ Capable of learning how to solve problem.
¨ But can incorporate prior knowledge as well.
¨ Can solve previously intractable problems.
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106. Starting Points
n World wide web
n Books and surveys
¨ Larrañaga & Lozano (eds.) (2001). Estimation of distribution
algorithms: A new tool for evolutionary computation. Kluwer.
¨ Pelikan et al. (2002). A survey to optimization by building and
using probabilistic models. Computational optimization and
applications, 21(1), pp. 5-20.
¨ Pelikan (2005). Hierarchical BOA: Towards a New Generation of
Evolutionary Algorithms. Springer.
¨ Lozano, Larrañaga, Inza, Bengoetxea (2006). Towards a New
Evolutionary Computation: Advances on Estimation of Distribution
Algorithms, Springer.
¨ Pelikan, Sastry, Cantu-Paz (eds.) (2006). Scalable Optimization via
Probabilistic Modeling: From Algorithms to Applications, Springer.
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107. Online Code (1/2)
n BOA, BOA with decision graphs, dependency-tree EDA
http://medal-lab.org/
n ECGA, xi-ary ECGA, BOA, and BOA with decision trees/graphs
http://www.illigal.org/
n mBOA
http://jiri.ocenasek.com/
n PIPE
http://www.idsia.ch/~rafal/
n Real-coded BOA
http://www.evolution.re.kr/
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108. Online Code (2/2)
n Demos of APS and EHBSA
http://www.hannan-u.ac.jp/~tsutsui/research-e.html
n RM-MEDA: A Regularity Model Based Multiobjective EDA
Differential Evolution + EDA hybrid
http://cswww.essex.ac.uk/staff/qzhang/mypublication.htm
n Naive Multi-objective Mixture-based IDEA (MIDEA)
Normal IDEA-Induced Chromosome Elements Exchanger (ICE)
Normal Iterated Density-Estimation Evolutionary Algorithm (IDEA)
http://homepages.cwi.nl/~bosman/code.html
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