This document summarizes a research paper that proposes a genetic algorithm called GeneAS that can handle optimization problems with mixed variable types. GeneAS uses binary coding for discrete variables and real coding for continuous variables. It employs different genetic operators for each variable type, such as binary crossover for discrete variables and SBX crossover for continuous variables. The paper describes these genetic operators and presents results on example problems that show GeneAS finds very good optimal solutions.
Metaheuristic Algorithms: A Critical AnalysisXin-She Yang
The document discusses metaheuristic algorithms and their application to optimization problems. It provides an overview of several nature-inspired algorithms including particle swarm optimization, firefly algorithm, harmony search, and cuckoo search. It describes how these algorithms were inspired by natural phenomena like swarming behavior, flashing fireflies, and bird breeding. The document also discusses applications of these algorithms to engineering design problems like pressure vessel design and gear box design optimization.
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.
Particle swarm optimization (PSO) is an evolutionary computation technique for optimizing problems. It initializes a population of random solutions and searches for optima by updating generations. Each potential solution, called a particle, tracks its best solution and the overall best solution to change its velocity and position in search of better solutions. The algorithm involves initializing particles with random positions and velocities, then updating velocities and positions iteratively based on the particles' local best solution and the global best solution until termination criteria are met. PSO has advantages of being simple, quick, and effective at locating good solutions.
Genetic algorithms are a type of evolutionary algorithm inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems over multiple generations. Genetic algorithms work on a population of potential solutions encoded as chromosomes, evolving them toward better solutions. They have been applied to optimization and search problems in various domains like robotics, engineering and bioinformatics.
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.
Dr. Ahmed Fouad Ali of Suez Canal University presents an overview of particle swarm optimization (PSO), a meta-heuristic optimization technique inspired by swarm intelligence in animals. PSO was proposed in 1995 by Kennedy and Eberhart and simulates the social behavior of bird flocking or fish schooling. In PSO, each potential solution is a "particle" moving in the search space, adjusting its position based on its own experience and the experience of neighboring particles. The algorithm tracks the best solution found by each particle and the best solution found by the entire swarm to guide the particles toward promising regions of the search space. PSO has advantages of being simple to implement with few parameters to adjust, while also being effective
Nature-Inspired Optimization Algorithms Xin-She Yang
This document discusses nature-inspired optimization algorithms. It begins with an overview of the essence of optimization algorithms and their goal of moving to better solutions. It then discusses some issues with traditional algorithms and how nature-inspired algorithms aim to address these. Several nature-inspired algorithms are described in detail, including particle swarm optimization, firefly algorithm, cuckoo search, and bat algorithm. These are inspired by behaviors in swarms, fireflies, cuckoos, and bats respectively. Examples of applications to engineering design problems are also provided.
Metaheuristic Algorithms: A Critical AnalysisXin-She Yang
The document discusses metaheuristic algorithms and their application to optimization problems. It provides an overview of several nature-inspired algorithms including particle swarm optimization, firefly algorithm, harmony search, and cuckoo search. It describes how these algorithms were inspired by natural phenomena like swarming behavior, flashing fireflies, and bird breeding. The document also discusses applications of these algorithms to engineering design problems like pressure vessel design and gear box design optimization.
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.
Particle swarm optimization (PSO) is an evolutionary computation technique for optimizing problems. It initializes a population of random solutions and searches for optima by updating generations. Each potential solution, called a particle, tracks its best solution and the overall best solution to change its velocity and position in search of better solutions. The algorithm involves initializing particles with random positions and velocities, then updating velocities and positions iteratively based on the particles' local best solution and the global best solution until termination criteria are met. PSO has advantages of being simple, quick, and effective at locating good solutions.
Genetic algorithms are a type of evolutionary algorithm inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems over multiple generations. Genetic algorithms work on a population of potential solutions encoded as chromosomes, evolving them toward better solutions. They have been applied to optimization and search problems in various domains like robotics, engineering and bioinformatics.
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. The algorithm is widely used and rapidly developed for its easy implementation and few particles required to be tuned. The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized. At last this paper presents some kinds of improved versions of PSO and research situation, and the future research issues are also given.
Dr. Ahmed Fouad Ali of Suez Canal University presents an overview of particle swarm optimization (PSO), a meta-heuristic optimization technique inspired by swarm intelligence in animals. PSO was proposed in 1995 by Kennedy and Eberhart and simulates the social behavior of bird flocking or fish schooling. In PSO, each potential solution is a "particle" moving in the search space, adjusting its position based on its own experience and the experience of neighboring particles. The algorithm tracks the best solution found by each particle and the best solution found by the entire swarm to guide the particles toward promising regions of the search space. PSO has advantages of being simple to implement with few parameters to adjust, while also being effective
Nature-Inspired Optimization Algorithms Xin-She Yang
This document discusses nature-inspired optimization algorithms. It begins with an overview of the essence of optimization algorithms and their goal of moving to better solutions. It then discusses some issues with traditional algorithms and how nature-inspired algorithms aim to address these. Several nature-inspired algorithms are described in detail, including particle swarm optimization, firefly algorithm, cuckoo search, and bat algorithm. These are inspired by behaviors in swarms, fireflies, cuckoos, and bats respectively. Examples of applications to engineering design problems are also provided.
Genetic algorithms are optimization techniques inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems by iteratively trying random variations. The document outlines the history, concepts, process and applications of genetic algorithms, including using them to optimize engineering design, routing, computer games and more. It describes how genetic algorithms encode potential solutions and use fitness functions to guide the evolution toward better outcomes.
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. Cuckoo search is a nature-inspired metaheuristic algorithm, based on the brood parasitism of some cuckoo species, along with Levy flights random walks
Particle swarm optimization is a metaheuristic algorithm inspired by the social behavior of bird flocking. It works by having a population of candidate solutions, called particles, that fly through the problem space, adjusting their positions based on their own experience and the experience of neighboring particles. Each particle keeps track of its best position and the best position of its neighbors. The algorithm iteratively updates the velocity and position of each particle to move it closer to better solutions.
This document provides an introduction to genetic algorithms. It explains that genetic algorithms are inspired by Darwinian evolution and use processes like selection, crossover and mutation to iteratively improve a population of potential solutions. It discusses how genetic algorithms can be used for optimization problems and classification in data mining. Examples of genetic algorithm applications like the traveling salesman problem are also presented to illustrate genetic algorithm concepts and processes.
This document discusses multi-objective optimization. It begins by defining multi-objective optimization as involving more than one objective function to optimize simultaneously. Objectives are often conflicting. The notion of an optimum must be redefined as a set of Pareto optimal solutions. Weighted sum methods are commonly used to generate Pareto optimal solutions by converting the multi-objective problem into single objective problems, but this has limitations such as an inability to find non-convex portions of the Pareto front. Evolutionary algorithms are now often used for multi-objective optimization.
The document discusses Particle Swarm Optimization (PSO) algorithms and their application in engineering design optimization. It provides an overview of optimization problems and algorithms. PSO is introduced as an evolutionary computational technique inspired by animal social behavior that can be used to find global optimization solutions. The document outlines the basic steps of the PSO algorithm and how it works by updating particle velocities and positions to track the best solutions. Examples of applications to model fitting and inductor design optimization are provided.
This document provides an overview of genetic algorithms. It discusses how genetic algorithms are inspired by natural evolution and use techniques like selection, crossover, and mutation to arrive at optimal solutions. The document covers the history of genetic algorithms, how they work, examples of using genetic algorithms to optimize problems, and their applications in fields like electromagnetism. Genetic algorithms provide a way to find optimal solutions to complex problems by simulating the natural evolutionary process of reproduction, mutation, and selection of offspring.
A brief introduction on the principles of particle swarm optimizaton by Rajorshi Mukherjee. This presentation has been compiled from various sources (not my own work) and proper references have been made in the bibliography section for further reading. This presentation was made as a presentation for submission for our college subject Soft Computing.
Genetic algorithms are a type of evolutionary algorithm that mimics natural selection. They operate on a population of potential solutions applying operators like selection, crossover and mutation to produce the next generation. The algorithm iterates until a termination condition is met, such as a solution being found or a maximum number of generations being produced. Genetic algorithms are useful for optimization and search problems as they can handle large, complex search spaces. However, they require properly defining the fitness function and tuning various parameters like population size, mutation rate and crossover rate.
Optimization and particle swarm optimization (O & PSO) Engr Nosheen Memon
The document discusses particle swarm optimization (PSO) which is a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. It summarizes PSO as follows: PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate is adjusted based on the best candidates in the local neighborhood and overall population. This process is repeated until a termination criterion is met.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
This document discusses genetic algorithms and provides an overview of their key concepts and components. It describes how genetic algorithms are inspired by Darwinian evolution and use techniques like selection, crossover and mutation to evolve solutions to optimization problems. It also outlines various parameters and strategies used in genetic algorithms, including chromosome representation, population size, selection methods, and termination criteria. A wide range of applications are mentioned where genetic algorithms have been applied successfully.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5.
---------------------------------
Read more about GA:
Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010.
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
Genetic algorithms (GAs) are optimization algorithms inspired by Darwinian evolution. They use techniques like mutation, crossover, and selection to evolve solutions to problems iteratively. The document provides examples to illustrate how GAs work, including finding a binary number and fitting a polynomial to data points. GAs initialize a population of random solutions, then improve it over generations by keeping the fittest solutions and breeding them using crossover and mutation to produce new solutions, until finding an optimal or near-optimal solution.
Multidisciplinary Design Optimization of Supersonic Transport Wing Using Surr...Masahiro Kanazaki
The document summarizes research on the multidisciplinary design optimization of supersonic transport wing concepts. The objectives were to develop an efficient MDO tool for conceptual design of SST wings using low-cost computational fluid dynamics and surrogate modeling. 107 design samples were evaluated for aerodynamic performance, sonic boom levels, and wing weight. 24 non-dominated solutions were identified, with the best designs having thinner root airfoils, larger inboard sweep angles, or modified tail angles compared to a baseline compromised design. Analysis of variance identified key design variables with the most influence on objectives.
This document proposes a self-adaptive version of simulated binary crossover (SBX) for real-parameter optimization. SBX is commonly used in evolutionary algorithms but its distribution index parameter is typically fixed, which can limit performance. The proposed method adapts the distribution index each generation based on how offspring compare to parents in terms of fitness. It derives an equation to update the index value based on whether offspring outperform parents. The goal is to improve SBX's ability to solve complex optimization problems by dynamically adjusting its behavior during a run.
Genetic algorithms are optimization techniques inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems by iteratively trying random variations. The document outlines the history, concepts, process and applications of genetic algorithms, including using them to optimize engineering design, routing, computer games and more. It describes how genetic algorithms encode potential solutions and use fitness functions to guide the evolution toward better outcomes.
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. Cuckoo search is a nature-inspired metaheuristic algorithm, based on the brood parasitism of some cuckoo species, along with Levy flights random walks
Particle swarm optimization is a metaheuristic algorithm inspired by the social behavior of bird flocking. It works by having a population of candidate solutions, called particles, that fly through the problem space, adjusting their positions based on their own experience and the experience of neighboring particles. Each particle keeps track of its best position and the best position of its neighbors. The algorithm iteratively updates the velocity and position of each particle to move it closer to better solutions.
This document provides an introduction to genetic algorithms. It explains that genetic algorithms are inspired by Darwinian evolution and use processes like selection, crossover and mutation to iteratively improve a population of potential solutions. It discusses how genetic algorithms can be used for optimization problems and classification in data mining. Examples of genetic algorithm applications like the traveling salesman problem are also presented to illustrate genetic algorithm concepts and processes.
This document discusses multi-objective optimization. It begins by defining multi-objective optimization as involving more than one objective function to optimize simultaneously. Objectives are often conflicting. The notion of an optimum must be redefined as a set of Pareto optimal solutions. Weighted sum methods are commonly used to generate Pareto optimal solutions by converting the multi-objective problem into single objective problems, but this has limitations such as an inability to find non-convex portions of the Pareto front. Evolutionary algorithms are now often used for multi-objective optimization.
The document discusses Particle Swarm Optimization (PSO) algorithms and their application in engineering design optimization. It provides an overview of optimization problems and algorithms. PSO is introduced as an evolutionary computational technique inspired by animal social behavior that can be used to find global optimization solutions. The document outlines the basic steps of the PSO algorithm and how it works by updating particle velocities and positions to track the best solutions. Examples of applications to model fitting and inductor design optimization are provided.
This document provides an overview of genetic algorithms. It discusses how genetic algorithms are inspired by natural evolution and use techniques like selection, crossover, and mutation to arrive at optimal solutions. The document covers the history of genetic algorithms, how they work, examples of using genetic algorithms to optimize problems, and their applications in fields like electromagnetism. Genetic algorithms provide a way to find optimal solutions to complex problems by simulating the natural evolutionary process of reproduction, mutation, and selection of offspring.
A brief introduction on the principles of particle swarm optimizaton by Rajorshi Mukherjee. This presentation has been compiled from various sources (not my own work) and proper references have been made in the bibliography section for further reading. This presentation was made as a presentation for submission for our college subject Soft Computing.
Genetic algorithms are a type of evolutionary algorithm that mimics natural selection. They operate on a population of potential solutions applying operators like selection, crossover and mutation to produce the next generation. The algorithm iterates until a termination condition is met, such as a solution being found or a maximum number of generations being produced. Genetic algorithms are useful for optimization and search problems as they can handle large, complex search spaces. However, they require properly defining the fitness function and tuning various parameters like population size, mutation rate and crossover rate.
Optimization and particle swarm optimization (O & PSO) Engr Nosheen Memon
The document discusses particle swarm optimization (PSO) which is a population-based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. It summarizes PSO as follows: PSO initializes a population of random solutions and searches for optima by updating generations of candidate solutions. Each candidate is adjusted based on the best candidates in the local neighborhood and overall population. This process is repeated until a termination criterion is met.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
This document discusses genetic algorithms and provides an overview of their key concepts and components. It describes how genetic algorithms are inspired by Darwinian evolution and use techniques like selection, crossover and mutation to evolve solutions to optimization problems. It also outlines various parameters and strategies used in genetic algorithms, including chromosome representation, population size, selection methods, and termination criteria. A wide range of applications are mentioned where genetic algorithms have been applied successfully.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This presentation gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs. A step-by-step example is given in addition to its implementation in Python 3.5.
---------------------------------
Read more about GA:
Yu, Xinjie, and Mitsuo Gen. Introduction to evolutionary algorithms. Springer Science & Business Media, 2010.
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
Transformer modality is an established architecture in natural language processing that utilizes a framework of self-attention with a deep learning approach.
This presentation was delivered under the mentorship of Mr. Mukunthan Tharmakulasingam (University of Surrey, UK), as a part of the ScholarX program from Sustainable Education Foundation.
Genetic algorithms (GAs) are optimization algorithms inspired by Darwinian evolution. They use techniques like mutation, crossover, and selection to evolve solutions to problems iteratively. The document provides examples to illustrate how GAs work, including finding a binary number and fitting a polynomial to data points. GAs initialize a population of random solutions, then improve it over generations by keeping the fittest solutions and breeding them using crossover and mutation to produce new solutions, until finding an optimal or near-optimal solution.
Multidisciplinary Design Optimization of Supersonic Transport Wing Using Surr...Masahiro Kanazaki
The document summarizes research on the multidisciplinary design optimization of supersonic transport wing concepts. The objectives were to develop an efficient MDO tool for conceptual design of SST wings using low-cost computational fluid dynamics and surrogate modeling. 107 design samples were evaluated for aerodynamic performance, sonic boom levels, and wing weight. 24 non-dominated solutions were identified, with the best designs having thinner root airfoils, larger inboard sweep angles, or modified tail angles compared to a baseline compromised design. Analysis of variance identified key design variables with the most influence on objectives.
This document proposes a self-adaptive version of simulated binary crossover (SBX) for real-parameter optimization. SBX is commonly used in evolutionary algorithms but its distribution index parameter is typically fixed, which can limit performance. The proposed method adapts the distribution index each generation based on how offspring compare to parents in terms of fitness. It derives an equation to update the index value based on whether offspring outperform parents. The goal is to improve SBX's ability to solve complex optimization problems by dynamically adjusting its behavior during a run.
PR-297: Training data-efficient image transformers & distillation through att...Jinwon Lee
안녕하세요 TensorFlow Korea 논문 읽기 모임 PR-12의 297번째 리뷰입니다
어느덧 PR-12 시즌 3의 끝까지 논문 3편밖에 남지 않았네요.
시즌 3가 끝나면 바로 시즌 4의 새 멤버 모집이 시작될 예정입니다. 많은 관심과 지원 부탁드립니다~~
(멤버 모집 공지는 Facebook TensorFlow Korea 그룹에 올라올 예정입니다)
오늘 제가 리뷰한 논문은 Facebook의 Training data-efficient image transformers & distillation through attention 입니다.
Google에서 나왔던 ViT논문 이후에 convolution을 전혀 사용하지 않고 오직 attention만을 이용한 computer vision algorithm에 어느때보다 관심이 높아지고 있는데요
이 논문에서 제안한 DeiT 모델은 ViT와 같은 architecture를 사용하면서 ViT가 ImageNet data만으로는 성능이 잘 안나왔던 것에 비해서
Training 방법 개선과 새로운 Knowledge Distillation 방법을 사용하여 mageNet data 만으로 EfficientNet보다 뛰어난 성능을 보여주는 결과를 얻었습니다.
정말 CNN은 이제 서서히 사라지게 되는 것일까요? Attention이 computer vision도 정복하게 될 것인지....
개인적으로는 당분간은 attention 기반의 CV 논문이 쏟아질 거라고 확신하고, 또 여기에서 놀라운 일들이 일어날 수 있을 거라고 생각하고 있습니다
CNN은 10년간 많은 연구를 통해서 발전해왔지만, transformer는 이제 CV에 적용된 지 얼마 안된 시점이라서 더 기대가 크구요,
attention이 inductive bias가 가장 적은 형태의 모델이기 때문에 더 놀라운 이들을 만들 수 있을거라고 생각합니다
얼마 전에 나온 open AI의 DALL-E도 그 대표적인 예라고 할 수 있을 것 같습니다. Transformer의 또하나의 transformation이 궁금하신 분들은 아래 영상을 참고해주세요
영상링크: https://youtu.be/DjEvzeiWBTo
논문링크: https://arxiv.org/abs/2012.12877
Condition Monitoring Of Unsteadily Operating EquipmentJordan McBain
The document discusses techniques for condition monitoring of unsteadily operating equipment. It proposes a statistical parameterization approach involving segmenting vibration data based on steady speeds/loads, extracting statistical parameters from segments, and using novelty detection with support vectors to classify patterns as normal or faulted while accounting for changing operating conditions. Experimental results on gearbox data demonstrated superior fault detection performance compared to alternative approaches.
Slides from our PacificVis 2015 presentation.
The paper tackles the problems of the “giant hairballs”, the dense and tangled structures often resulting from visualiza- tion of large social graphs. Proposed is a high-dimensional rotation technique called AGI3D, combined with an ability to filter elements based on social centrality values. AGI3D is targeted for a high-dimensional embedding of a social graph and its projection onto 3D space. It allows the user to ro- tate the social graph layout in the high-dimensional space by mouse dragging of a vertex. Its high-dimensional rotation effects give the user an illusion that he/she is destructively reshaping the social graph layout but in reality, it assists the user to find a preferred positioning and direction in the high- dimensional space to look at the internal structure of the social graph layout, keeping it unmodified. A prototype im- plementation of the proposal called Social Viewpoint Finder is tested with about 70 social graphs and this paper reports four of the analysis results.
Randomness conductors are a general framework that unifies various combinatorial objects like expanders, extractors, condensers, and universal hash functions. They can transform a probability distribution X with a certain amount of "entropy" into another distribution X' with a specified amount of entropy. The document discusses how expanders, extractors, and other objects are special cases of randomness conductors. It also describes how zigzag graph products can be used to construct explicit constant-degree randomness conductors and discusses some open problems in further studying and constructing these objects.
A Measure Of Independence For A Multifariate Normal Distribution And Some Con...gueste63bd9
This document discusses a measure of independence for multivariate normal distributions proposed by Rudas, Clogg and Lindsay. It presents results for the population value of this measure when applied to a general multivariate normal distribution compared to the simpler hypothesis of independent normal variables. Specifically, it shows that calculating this measure can be formulated as maximizing a concave function over a convex set. Connections to factor analysis are also briefly mentioned.
A Measure Of Independence For A Multifariate Normal Distribution And Some Con...ganuraga
This document discusses a measure of independence for multivariate normal distributions introduced by Rudas, Clogg and Lindsay in 1994. The author provides optimization results to calculate the value of this measure for a general multivariate normal distribution. Specifically, the problem is reduced to maximizing a concave function over a convex set. An explicit solution is obtained for equicorrelated normal variables, and an example given of an algorithm to calculate the measure for a general multivariate normal. Connections to factor analysis are also briefly discussed.
This document summarizes a distributed cloud-based genetic algorithm framework called TunUp for tuning the parameters of data clustering algorithms. TunUp integrates existing machine learning libraries and implements genetic algorithm techniques to tune parameters like K (number of clusters) and distance measures for K-means clustering. It evaluates internal clustering quality metrics on sample datasets and tunes parameters to optimize a chosen metric like AIC. The document outlines TunUp's features, describes how it implements genetic algorithms and parallelization, and concludes it is an open solution for clustering algorithm evaluation, validation and tuning.
This document provides instructions for using a TI-Nspire calculator to calculate the distance between two points using the distance formula. It describes how to create a graph with two labeled points mapped to variables, create a split screen with a spreadsheet, and enter the distance formula using the mapped variables so the spreadsheet will automatically recalculate as the points are moved in the graph. The distance formula is displayed to be sqrt((x2-x1)^2+(y2-y1)^2).
Incremental and parallel computation of structural graph summaries for evolvi...Till Blume
This document presents an incremental and parallel algorithm for computing structural graph summaries of evolving graphs. The algorithm incrementally updates graph summaries when the input graph changes, which is often faster than recomputing from scratch. The algorithm partitions the graph and computes summaries in parallel. Experimental results on real-world and benchmark graphs show the incremental algorithm outperforms batch computation even when 50% of the graph changes. The algorithm runs in linear time with respect to graph changes and degree.
The document describes a generic algorithm for the F-deletion problem, where the goal is to remove at most k vertices from a graph such that the remaining graph does not contain graphs from F as minors. It shows that when F contains only planar graphs, the algorithm provides a constant-factor approximation. It analyzes special cases where the algorithm works with different constants, such as when the graph minus the solution is independent, a matching, or acyclic. It then discusses how the algorithm extends to more general graphs by exploiting that the graph minus solution must have bounded treewidth when F contains planar graphs.
On recent improvements in the conic optimizer in MOSEKedadk
The software package MOSEK is capable of solving large-scale sparse
conic quadratic optimization problems using an interior-point method.
In this talk we will present our recent improvements in the implementation.
Moreover, we will present numerical results demonstrating the performance of the implementation.
The document summarizes a lecture on packet routing algorithms for hypercubes, including analyzing the expected time for a random routing algorithm to route packets from source to destination in two phases. It then discusses primal-dual algorithms for solving multi-commodity flow problems on networks and how they maintain constraints for both the primal and dual optimization problems through an iterative process of adjusting primal and dual variables.
Call for paper 2012, hard copy of Certificate, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJCER, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, research and review articles, IJCER Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathematics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer review journal, indexed journal, research and review articles, engineering journal, www.ijceronline.com, research journals,
yahoo journals, bing journals, International Journal of Computational Engineering Research, Google journals, hard copy of Certificate,
journal of engineering, online Submission
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document presents a method for solving fuzzy assignment problems where costs are represented by linguistic variables and fuzzy numbers. Linguistic variables are used to convert qualitative cost data into quantitative fuzzy numbers. Yager's ranking method is applied to rank the fuzzy numbers, transforming the fuzzy assignment problem into a crisp one. The resulting crisp problem is then solved using the Hungarian method to find the optimal assignment that minimizes total cost. A numerical example demonstrates the approach, showing a fuzzy cost matrix converted to crisp values and solved. The method allows handling assignment problems with imprecise, qualitative cost data using fuzzy logic concepts.
On the Scalability of Graph Kernels Applied to Collaborative RecommendersJérôme KUNEGIS
We study the scalability of several recent graph kernel-based collaborative recommendation algorithms.
We compare the performance of several graph kernel-based
recommendation algorithms, focusing on runtime and recommendation accuracy with respect to the reduced rank of the subspace. We inspect the exponential and Laplacian exponential kernels, the resistance distance kernel, the regularized Laplacian kernel, and the stochastic diffusion kernel. Furthermore, we introduce new variants of kernels based on the graph
Laplacian which, in contrast to existing kernels, also allow
negative edge weights and thus negative ratings. We perform an evaluation on the Netflix Prize rating corpus on prediction and recommendation tasks, showing that dimensionality reduction not only makes prediction faster, but sometimes also more accurate.
This document summarizes a research paper that proposes a novel fuzzy logic based edge detection technique for digital images. The technique uses three linear spatial filters to generate edge strength values for each pixel, which are then used as inputs to a fuzzy inference system. Gaussian membership functions are used to map the edge strength values to linguistic variables of "Low", "Medium", and "High". Fuzzy rules are applied to modify the membership values to classify pixels as edges or non-edges. Experimental results show the fuzzy technique performs better than Sobel and Kirsch operators, producing smoother edges with less noise.
study Domain Transform for Edge-Aware Image and Video ProcessingChiamin Hsu
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Simulated Binary Crossover
1. A Combined Genetic Adaptive Search (GeneAS)
for Engineering Design
Kalyanmoy Deb and Mayank Goyal (India)
Journal of Computer Science and Informatics, 1996
volume 26, page 30-45
Reviewed by
Paskorn Champrasert
paskorn@cs.umb.edu
http://dssg.cs.umb.edu
3. Introduction
• The aim in an engineering design problem is to
minimize or maximize a design objective and
satisfy constraints.
• The optimization problem can be expressed in the
following form:
• The variable
vector X
represents a set
of variables.
xi , i = 1, 2, , N
October 24, 08 DSSG Group Meeting 3/31
4. A Simple Cantilever beam design problem:
• A cantilever beam is a beam that is rigidly supported at
one end and carries a certain load on the other end.
Design possibilities:
- Shape (circular or square)
binary design variable (0/1)
- Size
Diameter of circular shape
Side of square shape
discrete variable
because size are not available in any sizes
- Length
real variable
because we can cut beams at any length
- Material
discrete variable
(cast iron, aluminum, steel, brass)
October 24, 08 DSSG Group Meeting 4/31
5. Research Issues
• The optimization problem is involving with different types of
variables.
E.g.,
- For the material variable
if the steel is represented as 1 and cast iron is represented as 2,
no intermediate value (e.g., 1.4 ) is allowed.
binary-coded variable should be used.
- For the beam length variable (0-100)
if the length is represented by using 4 bit binary-coded variable
(0000-1111). The precision of the variable is only 100/16 = 6.25
no more precise value (e.g., 2.5) is allowed
real-coded variable should be used.
• Designers must rely on an optimization algorithms which is
flexible enough to handle a mixed type of variables
October 24, 08 DSSG Group Meeting 5/31
6. GeneAS
• The algorithm restricts its search only to the permissible
values of the design variables.
• GeneAS uses a combination of binary-coded and real-
coded GA, depending on the nature of the design
variables.
– Variables are coded differently
– Genetic Operations (crossover and mutation) are performed
differently for each variable.
E.g.,
- Beam length is real-coded variable
real value crossover and real value mutation are used.
- Beam diameter is binary-coded variable
binary value crossover and real value mutation are used.
October 24, 08 DSSG Group Meeting 6/31
7. Binary-Coded GA
• In a binary-coded GA, the variables are
represented in binary strings formed with 1 and 0.
• Variables can be expressed as :
( b1b2b3…bn ) , bi is ether 1 or 0
• E.g., 7-bit string representing the diameter in the
range of (3,130) mm, the precision in the solution
is 1 mm
(130-3)/(1111111B-0000000B)
= 127/127 = 1mm
October 24, 08 DSSG Group Meeting 7/31
8. Binary Genetic Operations
One-point Crossover
1 0 0 1
1) A single crossover point on both parents
1 1 1 0 string is selected.
2) All data beyond that crossover point in
Crossover point
either parent is swapped between the two
1 0 1 0 parent organisms.
1 1 0 1 The resulting organisms are the offspring:
Binary Mutation
1 1 1 0
Each bit of offspring has a probability
1 1 0 0 (mutation rate) to flip (0 1 or 1 0)
October 24, 08 DSSG Group Meeting 8/31
9. Important Property of One-Point Crossover
1) The average of the decoded parameter values is
the same before and after the crossover
operation.
1 0 0 1 9
Average = 11.5
1 1 1 0 14
1 0 1 0 10
Average = 11.5
1 1 0 1 13
October 24, 08 DSSG Group Meeting 9/31
11. Important Property of One-Point Crossover
2) Spread factor:
Spread factor β is defined as the ratio of the spread of
offspring points to that of the parent points:
c1 −c2
β= | p1 −p2 |
c1 c2
- Contracting Crossover β<1 p1 p2
The offspring points are enclosed by the parent points.
- Expanding Crossover β>1 c1 c2
The offspring points enclose the parent points. p1 p2
- Stationary Crossover β=1 c1 c2
The offspring points are the same as parent points p1 p2
October 24, 08 DSSG Group Meeting 11/31
12. Spread Factor Property
• Assume parents strings are two strings:
p1= (a1a2a3…a15)
p2 = (b1b2b3…b15).
• The crossover point is varied from (0,15)
The probability distribution:
-- The probability of occurrence of β ≈ 1 is more likely than
any other β value.
If the parents are closer, the spread of the two likely
offspring is also smaller.
If the spread of parents is more, the spread of likely
offspring is also large.
October 24, 08 DSSG Group Meeting 12/31
13. Real-Coded GA
• In a real-coded GA, the variables are represented in continuous
variables.
• Variables can be expressed as :
X , X is any number in [XL,XU]
XL is lower bound ,XU is upper bound
• The binary-coded genetic operations cannot be used for real-coded
variables.
• The genetic operations have to be modified in order to handle real-
coded variables
However, the proposed real-coded genetic operations should
reserve the binary-coded genetic operation properties.
October 24, 08 DSSG Group Meeting 13/31
14. SBX
Simulated Binary Crossover
• Simulated Binary Crossover (SBX) was proposed in
1995 by Deb and Agrawal.
• SBX was designed with respect to the one-point
crossover properties in binary-coded GA.
– Average Property:
The average of the decoded parameter values is the same
before and after the crossover operation.
– Spread Factor Property:
The probability of occurrence of spread factor β ≈ 1 is more
likely than any other β value.
October 24, 08 DSSG Group Meeting 14/31
15. SBX – Offspring Value
To reserve the average property, the offspring values
are calculated as:
Offspring: c1 c2
c1 = x − 1 β(p2 − p1 )
¯ 2 β<1 p1 p2
c2 = x + 1 β(p2 − p1 )
¯ 2
c1 c2
By, β>1
1
x=
¯ 2 (p1
+ p2 ) p1 p2
p2 > p1
c1 c2
So,
β=1 p1 p2
c=x
¯ ¯
October 24, 08 DSSG Group Meeting 15/31
16. Spread Factor (β) in SBX
• The probability distribution of β in SBX should be similar (e.g.
same shape) to the probability distribution of β in Binary-coded
crossover.
• This paper proposed probability c(β) = 0.5(n + 1)β n , β ≤ 1
distribution function as: 1
c(β) = 0.5(n + 1) β n+2 , β > 1
1.6
1.4
Probability Distribution
1.2
1.0 n=2
0.8
0.6
0.4
0.2
0.0
0 1 2 3 4 5 6 7
β
October 24, 08 DSSG Group Meeting 16/31
17. A large value of n gives a higher
probability for creating ‘near-parent’
contracting solutions.
c(β) = 0.5(n + 1)β n , β ≤ 1
expanding
1
c(β) = 0.5(n + 1) β n+2 , β > 1
Mostly, n=2 to 5
October 24, 08 DSSG Group Meeting 17/31
18. Spread Factor (β) as a random number
• β is a random number that follows the proposed
probability distribution function:
1.6
1.4
To get a β value,
Probability Distribution
1.2
1) A unified random number u
n=2
is generated [0,1]
1.0
0.8
0.6 2) Get β value that makes the
0.4
0.2
area under the curve = u
0.0
u
0 1 2 3 4 5 6 7
β Offspring:
β
c1 = x − 1 β(p2 − p1 )
¯ 2
c2 = x + 1 β(p2 − p1 )
¯ 2
October 24, 08 DSSG Group Meeting 18/31
19. Real Mutation
• Mutation probability (pm) in (0,1) is applied to each
variable.
• For each variable
If a unified random number um in (0,1) < pm
mutation operator is performed for the variable.
p: parent
c: offspring
It represent the maximum permissible change
in the parent
¯
δ is a random number. Its probability distribution function is:
October 24, 08 DSSG Group Meeting 19/31
20. ¯
± is generated the same process as the β
October 24, 08 DSSG Group Meeting 20/31
21. Combined GA (GeneAS)
• GeneAS
– Each variable is coded depending on its nature.
– The crossover operator and mutation operator have
to be applied variable by variable.
• If it is a binary-coded variable, the binary-genetic
operations are performed
• If it is a real-coded variable, the real-genetic operations are
performed.
– The fitness function and constraint function are
calculated as normal.
October 24, 08 DSSG Group Meeting 21/31
23. Experiments
• Two different problems are solved using GeneAS.
• Two different problems were solved using different
traditional optimization algorithms.
- Lagrange multiplier method
by Kannan and Kramer 1993
- Branch and Bound method
by Sandgren 1988
• GeneAS
Crossover Probability =0.9
Mutation Probability =0
Selection mechanism = ?
Population size =50
Max generation =?
October 24, 08 DSSG Group Meeting 23/31
25. • Since, all the four variables are discrete, each
variable is coded in six-bit binary string (64
values), so that variables take values between 12
and 75.
• Four constraints are added to the objective
function.
October 24, 08 DSSG Group Meeting 25/31
26. Result
GeneAS have found much better solutions than the
previously known optimal solutions.
October 24, 08 DSSG Group Meeting 26/31
27. Welded Beam Design
• A rectangular beam needs to be welded as a
cantilever beam to carry a certain load.
• The objective of the design of the welded beam
is to minimize the overall cost of the fabrication
which involves the cost of beam material and the
cost of weld deposit.
• Two different welding configurations can be
applied.
(1 is used for four-sided welding and 0 is used
for two-sided welding) : X1
October 24, 08 DSSG Group Meeting 27/31
28. • One of the four materials can be used
– (00-steel, 01-cast iron, 10-aluminum, 11-brass): X2
• h : X3
• t : X4
• b : X5
• l : X6
October 24, 08 DSSG Group Meeting 28/31
31. Conclusions
• This paper presents a proposed genetic
algorithms for engineering optimization
problems.
• The type variable can be different.
• SBX and real-mutation is proposed.
• The results show that GeneAS can find very
good optimal solutions.
October 24, 08 DSSG Group Meeting 31/31
34. BLX-α
• Blend Crossover (BLX-α) was proposed by Eshelman and
Schaffer (1993)
• BLX-α operator is used for real-parameter GAs.
• α represents a constant. (0,1)
• For two parents x1 and x2 (assume x1 < x2)
• BLX-α randomly picks a solution in the range
[x1- α(x2-x1) , x2+ α(x2-x1) ]
Possible offspring
x1 x2
α(x2-x1) α(x2-x1)
• The investigators have reported that BLX-0.5 (α=0.5)
performs better than BLX operators with other α value.
October 24, 08 DSSG Group Meeting 34/31