Parallel Problem Solving From Nature Ppsn X
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Parallel Problem Solving From Nature Ppsn X
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Author : Günter Rudolph
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-10
Parallel Problem Solving From Nature Ppsn X written by Günter Rudolph and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-10 with Computers categories.
This book constitutes the refereed proceedings of the 10th International Conference on Parallel Problem Solving from Nature, PPSN 2008, held in Dortmund, Germany, in September 2008. The 114 revised full papers presented were carefully reviewed and selected from 206 submissions. The conference covers a wide range of topics, such as evolutionary computation, quantum computation, molecular computation, neural computation, artificial life, swarm intelligence, artificial ant systems, artificial immune systems, self-organizing systems, emergent behaviors, and applications to real-world problems. The paper are organized in topical sections on formal theory, new techniques, experimental analysis, multiobjective optimization, hybrid methods, and applications.
Parallel Problem Solving From Nature Ppsn Iv
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Author : Werner Ebeling
language : en
Publisher: Springer
Release Date : 1996-09-04
Parallel Problem Solving From Nature Ppsn Iv written by Werner Ebeling and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-09-04 with Computers categories.
This book constitutes the refereed proceedings of the International Conference on Evolutionary Computation held jointly with the 4th Conference on Parallel Problem Solving from Nature, PPSN IV, in Berlin, Germany, in September 1996. The 103 revised papers presented in the volume were carefully selected from more than 160 submissions. The papers are organized in sections on basic concepts of evolutionary computation (EC), theoretical foundations of EC, modifications and extensions of evolutionary algorithms, comparison of methods, other metaphors, and applications of EC in a variety of areas like ML, NNs, engineering, CS, OR, and biology. The book has a comprehensive subject index.
Parallel Problem Solving From Nature Ppsn Viii
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Author : Xin Yao
language : en
Publisher: Springer Science & Business Media
Release Date : 2004-09-13
Parallel Problem Solving From Nature Ppsn Viii written by Xin Yao and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-09-13 with Computers categories.
This book constitutes the refereed proceedings of the 8th International Conference on Parallel Problem Solving from Nature, PPSN 2004, held in Birmingham, UK, in September 2004. The 119 revised full papers presented were carefully reviewed and selected from 358 submissions. The papers address all current issues in biologically inspired computing; they are organized in topical sections on theoretical and foundational issues, new algorithms, applications, multi-objective optimization, co-evolution, robotics and multi-agent systems, and learning classifier systems and data mining.
Parallel Problem Solving From Nature Ppsn Xvi
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Author : Thomas Bäck
language : en
Publisher: Springer Nature
Release Date : 2020-09-02
Parallel Problem Solving From Nature Ppsn Xvi written by Thomas Bäck and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-02 with Computers categories.
This two-volume set LNCS 12269 and LNCS 12270 constitutes the refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization.
Introduction To Evolutionary Algorithms
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Author : Xinjie Yu
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-10
Introduction To Evolutionary Algorithms written by Xinjie Yu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-10 with Computers categories.
Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.
Machine Learning For Evolution Strategies
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Author : Oliver Kramer
language : en
Publisher: Springer
Release Date : 2016-05-25
Machine Learning For Evolution Strategies written by Oliver Kramer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-25 with Technology & Engineering categories.
This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.
Parallel Problem Solving From Nature Ppsn Ix
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Author : Thomas Philip Runarsson
language : en
Publisher: Springer
Release Date : 2006-10-06
Parallel Problem Solving From Nature Ppsn Ix written by Thomas Philip Runarsson and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-10-06 with Computers categories.
This book constitutes the refereed proceedings of the 9th International Conference on Parallel Problem Solving from Nature, PPSN 2006. The book presents 106 revised full papers covering a wide range of topics, from evolutionary computation to swarm intelligence and bio-inspired computing to real-world applications. These are organized in topical sections on theory, new algorithms, applications, multi-objective optimization, evolutionary learning, as well as representations, operators, and empirical evaluation.
Multimodal Optimization By Means Of Evolutionary Algorithms
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Author : Mike Preuss
language : en
Publisher: Springer
Release Date : 2015-11-27
Multimodal Optimization By Means Of Evolutionary Algorithms written by Mike Preuss and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-27 with Computers categories.
This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used. The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis.
The Theory Of Evolution Strategies
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Author : Hans-Georg Beyer
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
The Theory Of Evolution Strategies written by Hans-Georg Beyer and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Computers categories.
Evolutionary Algorithms, in particular Evolution Strategies, Genetic Algorithms, or Evolutionary Programming, have found wide acceptance as robust optimization algorithms in the last ten years. Compared with the broad propagation and the resulting practical prosperity in different scientific fields, the theory has not progressed as much. This monograph provides the framework and the first steps toward the theoretical analysis of Evolution Strategies (ES). The main emphasis is on understanding the functioning of these probabilistic optimization algorithms in real-valued search spaces by investigating the dynamical properties of some well-established ES algorithms. The book introduces the basic concepts of this analysis, such as progress rate, quality gain, and self-adaptation response, and describes how to calculate these quantities. Based on the analysis, functioning principles are derived, aiming at a qualitative understanding of why and how ES algorithms work.
Hybrid Optimization
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Author : Pascal van Hentenryck
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-11-05
Hybrid Optimization written by Pascal van Hentenryck and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-05 with Mathematics categories.
Hybrid Optimization focuses on the application of artificial intelligence and operations research techniques to constraint programming for solving combinatorial optimization problems. This book covers the most relevant topics investigated in the last ten years by leading experts in the field, and speculates about future directions for research. This book includes contributions by experts from different but related areas of research including constraint programming, decision theory, operations research, SAT, artificial intelligence, as well as others. These diverse perspectives are actively combined and contrasted in order to evaluate their relative advantages. This volume presents techniques for hybrid modeling, integrated solving strategies including global constraints, decomposition techniques, use of relaxations, and search strategies including tree search local search and metaheuristics. Various applications of the techniques presented as well as supplementary computational tools are also discussed.