This document discusses elementary landscape decomposition for analyzing combinatorial optimization problems. It begins with definitions of landscapes, elementary landscapes, and landscape decomposition. Elementary landscapes have specific properties, like local maxima and minima. Any landscape can be decomposed into a set of elementary components. This decomposition provides insights into problem structure and can be used to design selection strategies and predict search performance. The document concludes that landscape decomposition is useful for understanding problems but methodology is still needed to decompose general landscapes.
C. Guyon, T. Bouwmans. E. Zahzah, “Foreground Detection via Robust Low Rank Matrix Decomposition including Spatio-Temporal Constraint”, International Workshop on Background Model Challenges, ACCV 2012, Daejon, Korea, November 2012.
Note on Coupled Line Cameras for Rectangle Reconstruction (ACDDE 2012)Joo-Haeng Lee
The presentation file for the talk in ACDDE 2012.
http://www.acdde2012.org/
It deals with the research result published in ICPR 2012 with the title as "Camera Calibration from a Single Image based on Coupled Line Cameras and Rectangle Constraint"
https://iapr.papercept.net/conferences/scripts/abstract.pl?ConfID=7&Number=70
In this article we consider macrocanonical models for texture synthesis. In these models samples are generated given an input texture image and a set of features which should be matched in expectation. It is known that if the images are quantized, macrocanonical models are given by Gibbs measures, using the maximum entropy principle. We study conditions under which this result extends to real-valued images. If these conditions hold, finding a macrocanonical model amounts to minimizing a convex function and sampling from an associated Gibbs measure. We analyze an algorithm which alternates between sampling and minimizing. We present experiments with neural network features and study the drawbacks and advantages of using this sampling scheme.
In many applications one observes rapid change of the solution in the boundary region. Accurate and numerically efficient resolution of the solution close to the moving boundaries is considered to be an important problem. We develop an approach to the optimization of the discretization grids for finite-difference scheme. Using the suggested approach we are able to achieve the exponential convergence of the boundary Neumann- to-Dirichlet maps. It increases the convergence order without increasing the stencil size of the finite-difference scheme and preserves stability.
C. Guyon, T. Bouwmans. E. Zahzah, “Foreground Detection via Robust Low Rank Matrix Factorization including Spatial Constraint with Iterative Reweighted Regression”, International Conference on Pattern Recognition, ICPR 2012, Tsukuba, Japan, November 2012.
Paper introduction to Combinatorial Optimization on Graphs of Bounded TreewidthYu Liu
This slides introduced the paper: H. L. Bodlaender and a. M. C. a. Koster, “Combinatorial Optimization on Graphs of Bounded Treewidth,” Comput. J., vol. 51, no. 3, pp. 255–269, Nov. 2007.
Plenary Speaker slides at the 2016 International Workshop on Biodesign Automa...Natalio Krasnogor
In this talk I discuss recent work done in my lab and with collaborators abroad that contributes towards accelerating the specify -> design -> model -> build -> test & iterate biological engineering cycle. This will describe advances in biological programming languages for specifying combinatorial DNA libraries, the utilisation of off-the-shelf microfluidic devices to build the DNA libraries as well as data analysis techniques to accelerate computational simulations
C. Guyon, T. Bouwmans. E. Zahzah, “Foreground Detection via Robust Low Rank Matrix Decomposition including Spatio-Temporal Constraint”, International Workshop on Background Model Challenges, ACCV 2012, Daejon, Korea, November 2012.
Note on Coupled Line Cameras for Rectangle Reconstruction (ACDDE 2012)Joo-Haeng Lee
The presentation file for the talk in ACDDE 2012.
http://www.acdde2012.org/
It deals with the research result published in ICPR 2012 with the title as "Camera Calibration from a Single Image based on Coupled Line Cameras and Rectangle Constraint"
https://iapr.papercept.net/conferences/scripts/abstract.pl?ConfID=7&Number=70
In this article we consider macrocanonical models for texture synthesis. In these models samples are generated given an input texture image and a set of features which should be matched in expectation. It is known that if the images are quantized, macrocanonical models are given by Gibbs measures, using the maximum entropy principle. We study conditions under which this result extends to real-valued images. If these conditions hold, finding a macrocanonical model amounts to minimizing a convex function and sampling from an associated Gibbs measure. We analyze an algorithm which alternates between sampling and minimizing. We present experiments with neural network features and study the drawbacks and advantages of using this sampling scheme.
In many applications one observes rapid change of the solution in the boundary region. Accurate and numerically efficient resolution of the solution close to the moving boundaries is considered to be an important problem. We develop an approach to the optimization of the discretization grids for finite-difference scheme. Using the suggested approach we are able to achieve the exponential convergence of the boundary Neumann- to-Dirichlet maps. It increases the convergence order without increasing the stencil size of the finite-difference scheme and preserves stability.
C. Guyon, T. Bouwmans. E. Zahzah, “Foreground Detection via Robust Low Rank Matrix Factorization including Spatial Constraint with Iterative Reweighted Regression”, International Conference on Pattern Recognition, ICPR 2012, Tsukuba, Japan, November 2012.
Paper introduction to Combinatorial Optimization on Graphs of Bounded TreewidthYu Liu
This slides introduced the paper: H. L. Bodlaender and a. M. C. a. Koster, “Combinatorial Optimization on Graphs of Bounded Treewidth,” Comput. J., vol. 51, no. 3, pp. 255–269, Nov. 2007.
Plenary Speaker slides at the 2016 International Workshop on Biodesign Automa...Natalio Krasnogor
In this talk I discuss recent work done in my lab and with collaborators abroad that contributes towards accelerating the specify -> design -> model -> build -> test & iterate biological engineering cycle. This will describe advances in biological programming languages for specifying combinatorial DNA libraries, the utilisation of off-the-shelf microfluidic devices to build the DNA libraries as well as data analysis techniques to accelerate computational simulations
In large scale visual pattern recognition applications, when the subject set is large the traditional linear models like PCA/LDA/LPP, become inadequate in capturing the non-linearity and local variations of visual appearance manifold. Kernelized solutions can alleviate the problem to certain degree, but faces a computational complexity challenge of solving eigen or QP problems of size n x n for number of training samples n. In this work, we developed a novel solution to this problem by applying a data partition first and obtain a rich set of local data patch models, then the hierarchical structure of this rich set of models are computed with subspace clustering on Grassmanian manifold, via a VQ like algorithm with data partition locality constraint. At query time, a probe image is projected to the data space partition first to obtain the probe model, and the optimal local model is computed by traversing the model hierarchical tree. Simulation results demonstrated the effectiveness of this solution in computational efficiency and recognition accuracy, with applications in large subject set face recognition and image retrieval.
[Bio]
Zhu Li is currently a Senior Staff Researcher and Media Analytics & Processing Group Lead with the Media Networking Lab, Core Networks Research, FutureWei (Huawei) Technology USA, at Bridgewater, New Jersey. He received his PhD in Electrical & Computer Engineering from Northwestern University, Evanston in 2004. He was an Assistant Professor with the Dept of Computing, The Hong Kong Polytechnic University from 2008 to 2010, and a Senior Research Engineer, Senior Staff Research Engineering, and then Principal Staff Research Engineer with the Multimedia Research Lab (MRL), Motorola Labs, Schaumburg, Illinois, from 2000 to 2008. His research interests include audio-visual analytics and machine learning with its application in large scale video repositories annotation, search and recommendation, as well as video adaptation, source-channel coding and distributed optimization issues of the wireless video networks. He has 21 issued or pending patents, 70+ publications in book chapters, journals, conference proceedings and standards contributions in these areas. He is an IEEE senior member, elected Vice Chair of the IEEE Multimedia Communication Technical Committee (MMTC) 2008~2010, co-editor for the Springer-Verlag book on "Intelligent Video Communication: Techniques and Applications". He served on numerous conference and workshop TPCs and was symposium co-chair at IEEE ICC'2008, and on Best Paper Award Committee for IEEE ICME 2010. He received the Best Poster Paper Award from IEEE Int'l Conf on Multimedia & Expo (ICME) at Toronto, 2006, and the Best Paper Award from IEEE Int'l Conf on Image Processing (ICIP) at San Antonio, 2007.
In topological inference, the goal is to extract information about a shape, given only a sample of points from it. There are many approaches to this problem, but the one we focus on is persistent homology. We get a view of the data at different scales by imagining the points are balls and consider different radii. The shape information we want comes in the form of a persistence diagram, which describes the components, cycles, bubbles, etc in the space that persist over a range of different scales.
To actually compute a persistence diagram in the geometric setting, previous work required complexes of size n^O(d). We reduce this complexity to O(n) (hiding some large constants depending on d) by using ideas from mesh generation.
This talk will not assume any knowledge of topology. This is joint work with Gary Miller, Benoit Hudson, and Steve Oudot.
Limits of Wiener filtering. Non-linear shrinkage functions. Limits of Fourier representation. Continuous and discrete wavelet transforms. Sparsity and shrinkage in wavelet domain. Undecimated wavelet transforms, a trous algorithm. Regularization. Sparse regression, combinatorial optimization and matching pursuit. LASSO, non-smooth optimization, and proximal minimization. Link with implcit Euler scheme. ISTA algorithm. Syntehsis versus analysis regularized models. Applications to image deconvolution.
Asynchronous Stochastic Optimization, New Analysis and AlgorithmsFabian Pedregosa
As datasets continue to increase in size and multi-core computer architectures are developed, asynchronous parallel optimization algorithms become more and more essential to the field of Machine Learning. In this talk I will describe two of our recent contributions to this topic. First, we highlight an important technical issue present in a large fraction of the recent convergence proofs for asynchronous parallel optimization algorithms and propose a new framework that resolves it [1]. Second, we propose a novel asynchronous variant of SAGA, a stochastic method that combines the low cost per iteration of SGD with the fast convergence rates of gradient descent [2]
[1] Leblond, R., Pedregosa, F., & Lacoste-Julien, S. (2018). Improved asynchronous parallel optimization analysis for stochastic incremental methods. arXiv:1801.03749, https://arxiv.org/pdf/1801.03749.pdf
[2] Pedregosa, F., Leblond, R., & Lacoste-Julien, S. (2017). Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization. In Advances in Neural Information Processing Systems, http://papers.nips.cc/paper/6611-breaking-the-nonsmooth-barrier-a-scalable-parallel-method-for-composite-optimization.pdf
Seminario-taller: Introducción a la Ingeniería del Software Guiada or Búsquedajfrchicanog
Transparencias utilizadas en el seminario-taller celebrado en el marco de los cursos de doctorado de la Universidad de Almería, los días 26 y 27 de octubre de 2020.
This is the presentation of the paper "Quasi-Optimal Recombination Operator" presented in EvoCOP 2019 (Best paper session). The paper is available in LNCS with doi: https://doi.org/10.1007/978-3-030-16711-0_9
Enhancing Partition Crossover with Articulation Points Analysisjfrchicanog
This is the presentation of the paper entitled "Enhancing Partition Crossover with Articulation Points Analysis" at the ECOM track in gECCO 2018 (Kyoto). This paper was awarded with a "Best Paper Award"
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Communications Mining Series - Zero to Hero - Session 1
Elementary Landscape Decomposition of Combinatorial Optimization Problems
1. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Elementary Landscape Decomposition of
Combinatorial Optimization Problems
Francisco Chicano
Work in collaboration with L. Darrell Whitley
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 1 / 17
2. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Motivation
Motivation
• Landscapes’ theory is a tool for analyzing optimization problems
• Peter F. Stadler is one of the main supporters of the theory
• Applications in Chemistry, Physics, Biology and Combinatorial Optimization
• Central idea: study the search space to obtain information
• Better understanding of the problem
• Predict algorithmic performance
• Improve search algorithms
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 2 / 17
3. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Landscape Definition Elementary Landscapes Landscape decomposition
Landscape Definition
• A landscape is a triple (X,N, f) where
X is the solution space The pair (X,N) is called
N is the neighbourhood operator configuration space
f is the objective function
• The neighbourhood operator is a function s0 5
N: X →P(X)
2 s1 s3 2
• Solution y is neighbour of x if y ∈ N(x) s2 3
s5 1
• Regular and symmetric neighbourhoods 0 s7
s4
4
• d=|N(x)| ∀x∈X s6 0
• y ∈ N(x) ⇔ x ∈ N(y) 6 s8
s9
7
• Objective function
f: X →R (or N, Z, Q)
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 3 / 17
4. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Landscape Definition Elementary Landscapes Landscape decomposition
Elementary Landscapes: Formal Definition
• An elementary function is an eigenvector of the graph Laplacian (plus constant)
Adjacency matrix Degree matrix
s0
• Graph Laplacian:
s1 s3
s2
Depends on the s5
configuration space s7
s4
• Elementary function: eigenvector of Δ (plus constant) s6
s8
s9
Eigenvalue
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 4 / 17
5. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Landscape Definition Elementary Landscapes Landscape decomposition
Elementary Landscapes: Characterizations
• An elementary landscape is a landscape for which
Depend on the
problem/instance
where Linear relationship
• Grover’s wave equation
Characteristic constant: k= - λ
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 5 / 17
6. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Landscape Definition Elementary Landscapes Landscape decomposition
Elementary Landscapes: Properties
• Some properties of elementary landscapes are the following
where
• Local maxima and minima
Local maxima
Local minima
X
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 6 / 17
7. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Landscape Definition Elementary Landscapes Landscape decomposition
Elementary Landscapes: Examples
Problem Neighbourhood d k
2-opt n(n-3)/2 n-1
Symmetric TSP
swap two cities n(n-1)/2 2(n-1)
inversions n(n-1)/2 n(n+1)/2
Antisymmetric TSP
swap two cities n(n-1)/2 2n
Graph α-Coloring recolor 1 vertex (α-1)n 2α
Graph Matching swap two elements n(n-1)/2 2(n-1)
Graph Bipartitioning Johnson graph n2/4 2(n-1)
NEAS bit-flip n 4
Max Cut bit-flip n 4
Weight Partition bit-flip n 4
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 7 / 17
8. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Landscape Definition Elementary Landscapes Landscape decomposition
Landscape Decomposition
• What if the landscape is not elementary?
• Any landscape can be written as the sum of elementary landscapes
X X X
• There exists a set of eigenfunctions of Δ that form a basis of the function
space (Fourier basis)
e2 Non-elementary function
Elementary functions f Elementary
components of f
(from the Fourier basis)
e1
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 8 / 17
9. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Landscape Definition Elementary Landscapes Landscape decomposition
Landscape Decomposition: Walsh Functions
• If X is the set of binary strings of length n and N is the bit-flip neighbourhood then a
Fourier basis is
where Bitwise AND
Bit count function
(number of ones)
• These functions are known as Walsh Functions
• The function with subindex w is elementary with k=2 bc (w)
• In general, decomposing a landscape is not a trivial task → methodology required
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 9 / 17
10. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Landscape Definition Elementary Landscapes Landscape decomposition
Landscape Decomposition: Examples
Problem Neighbourhood d Components
inversions n(n-1)/2 2
General TSP
swap two cities n(n-1)/2 2
QAP swap two elements n(n-1)/2 3
Frequency Assignment change 1 frequency (α-1)n 2
Subset Sum Problem bit-flip n 2
MAX k-SAT bit-flip n k
NK-landscapes bit-flip n k+1
max. nb. of
Radio Network Design bit-flip n reachable
antennae
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 10 / 17
11. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Selection Strategy Autocorrelation
New Selection Strategy
• Selection operators usually take into account the fitness value of the individuals
avg
avg Minimizing
Neighbourhoods
X
• We can improve the selection operator by selecting the individuals according to
the average value in their neighbourhoods
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 11 / 17
12. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Selection Strategy Autocorrelation
New Selection Strategy
• In elementary landscapes the traditional and the new operator are the same!
Recall that...
• However, they are not the same in non-elementary landscapes. If we have n
elementary components, then:
Elementary components
• The new selection strategy could be useful for plateaus
Minimizing
avg avg
X
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 12 / 17
13. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Selection Operator Autocorrelation
Autocorrelation
• Let {x0, x1, ...} a simple random walk on the configuration space where xi+1∈N(xi)
• The random walk induces a time series {f(x0), f(x1), ...} on a landscape.
• The autocorrelation function is defined as: s0
5
2 2
s1 s3
3
s2 1
s5
0
• The autocorrelation length is defined as: s7
4 s4 0
s6
6
s8
s9
7
• Autocorrelation length conjecture:
The number of local optima in a search space is roughly Solutions
reached from x0
after l moves
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 13 / 17
14. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Selection Operator Autocorrelation
Autocorrelation Length Conjecture
• The higher the value of l the smaller the number of local optima and the better the
performance of a local search method
Angel, Zissimopoulos. Theoretical
• l is a measure of the ruggedness of a landscape Computer Science 264:159-172
Nb. steps (config 1) Nb. steps (config 2)
Length Ruggedness
% rel. error nb. steps % rel. error nb. steps
10 ≤ ζ < 20 0.2 50500 0.1 101395
20 ≤ ζ < 30 0.3 53300 0.2 106890
30 ≤ ζ < 40 0.3 58700 0.2 118760
40 ≤ ζ < 50 0.5 62700 0.3 126395
50 ≤ ζ < 60 0.7 66100 0.4 133055
60 ≤ ζ < 70 1.0 75300 0.6 151870
70 ≤ ζ < 80 1.3 76800 1.0 155230
80 ≤ ζ < 90 1.9 79700 1.4 159840
90 ≤ ζ < 100 2.0 82400 1.8 165610
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 14 / 17
15. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Selection Operator Autocorrelation
Autocorrelation and Landscapes
• If f is a sum of elementary landscapes: Fourier coefficients
• For elementary landscapes:
• Using the landscape decomposition we can determine a priori the performance of
a local search method
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 15 / 17
16. Background on Conclusions
Introduction Applications
Landscapes & Future Work
Conclusions & Future Work
Conclusions & Future Work
Conclusions
• Elementary landscape decomposition is a useful tool to understand a problem
• The decomposition can be used to design new operators
• We can exactly determine the autocorrelation functions
• It is not easy to find a decomposition in the general case
Future Work
• Methodology for landscape decomposition
• Search for additional applications of landscapes’ theory in EAs
• Design new operators and search methods based on landscapes’ information
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 16 / 17
17. Elementary Landscape Decomposition of
Combinatorial Optimization Problems
Thanks for your attention !!!
Evolutionary Algorithms - Challenges in Theory and Practice, Bourdeaux, March 2010 17 / 17