Download or read book Noisy Optimization With Evolution Strategies written by Dirk V. Arnold and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, stochastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary algorithms are general, nature-inspired heuristics for numerical search and optimization that are frequently observed to be particularly robust with regard to the effects of noise. Noisy Optimization with Evolution Strategies contributes to the understanding of evolutionary optimization in the presence of noise by investigating the performance of evolution strategies, a type of evolutionary algorithm frequently employed for solving real-valued optimization problems. By considering simple noisy environments, results are obtained that describe how the performance of the strategies scales with both parameters of the problem and of the strategies considered. Such scaling laws allow for comparisons of different strategy variants, for tuning evolution strategies for maximum performance, and they offer insights and an understanding of the behavior of the strategies that go beyond what can be learned from mere experimentation. This first comprehensive work on noisy optimization with evolution strategies investigates the effects of systematic fitness overvaluation, the benefits of distributed populations, and the potential of genetic repair for optimization in the presence of noise. The relative robustness of evolution strategies is confirmed in a comparison with other direct search algorithms. Noisy Optimization with Evolution Strategies is an invaluable resource for researchers and practitioners of evolutionary algorithms.
Download or read book Principles in Noisy Optimization written by Pratyusha Rakshit and published by Springer. This book was released on 2018-11-20 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Noisy optimization is a topic of growing interest for researchers working on mainstream optimization problems. Although several techniques for dealing with stochastic noise in optimization problems are covered in journals and conference proceedings, today there are virtually no books that approach noisy optimization from a layman’s perspective; this book remedies that gap. Beginning with the foundations of evolutionary optimization, the book subsequently explores the principles of noisy optimization in single and multi-objective settings, and presents detailed illustrations of the principles developed for application in real-world multi-agent coordination problems. Special emphasis is given to the design of intelligent algorithms for noisy optimization in real-time applications. The book is unique in terms of its content, writing style and above all its simplicity, which will appeal to readers with a broad range of backgrounds. The book is divided into 7 chapters, the first of which provides an introduction to Swarm and Evolutionary Optimization algorithms. Chapter 2 includes a thorough review of agent architectures for multi-agent coordination. In turn, Chapter 3 provides an extensive review of noisy optimization, while Chapter 4 addresses issues of noise handling in the context of single-objective optimization problems. An illustrative case study on multi-robot path-planning in the presence of measurement noise is also highlighted in this chapter. Chapter 5 deals with noisy multi-objective optimization and includes a case study on noisy multi-robot box-pushing. In Chapter 6, the authors examine the scope of various algorithms in noisy optimization problems. Lastly, Chapter 7 summarizes the main results obtained in the previous chapters and elaborates on the book’s potential with regard to real-world noisy optimization problems.
Download or read book Contributions to Convergence Analysis of Noisy Optimization Algorithms written by Sandra Astete morales and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis exposes contributions to the analysis of algorithms for noisy functions. It exposes convergence rates for linesearch algorithms as well as for random search algorithms. We prove in terms of Simple Regret and Cumulative Regret that a Hessian based algorithm can reach the same results as some optimal algorithms in the literature, when parameters are tuned correctly. On the other hand we analyse the convergence order of Evolution Strategies when solving noisy functions. We deduce log-log convergence. We also give a lower bound for the convergence rate of the Evolution Strategies. We extend the work on revaluation by applying it to a discrete settings. Finally we analyse the performance measure itself and prove that the use of an erroneus performance measure can lead to misleading results on the evaluation of different methods.
Download or read book Learning and Intelligent Optimization written by Ilias S. Kotsireas and published by Springer Nature. This book was released on 2020-07-17 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 14, held in Athens, Greece, in May 2020. The 37 full papers presented together with one invited paper have been carefully reviewed and selected from 75 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components. Due to the COVID-19 pandemic, LION 14 was not held as a physical meeting.
Download or read book Springer Handbook of Computational Intelligence written by Janusz Kacprzyk and published by Springer. This book was released on 2015-05-28 with total page 1637 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Springer Handbook for Computational Intelligence is the first book covering the basics, the state-of-the-art and important applications of the dynamic and rapidly expanding discipline of computational intelligence. This comprehensive handbook makes readers familiar with a broad spectrum of approaches to solve various problems in science and technology. Possible approaches include, for example, those being inspired by biology, living organisms and animate systems. Content is organized in seven parts: foundations; fuzzy logic; rough sets; evolutionary computation; neural networks; swarm intelligence and hybrid computational intelligence systems. Each Part is supervised by its own Part Editor(s) so that high-quality content as well as completeness are assured.
Download or read book Modified Selection Mechanisms Designed to Help Evolution Strategies Cope with Noisy Response Surfaces written by Sriphani Raju Gadiraju and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the rise in the application of evolution strategies for simulation optimization, a better understanding of how these algorithms are affected by the stochastic output produced by simulation models is needed. At very high levels of stochastic variance in the output, evolution strategies in their standard form experience difficulty locating the optimum. The degradation of the performance of evolution strategies in the presence of very high levels of variation can be attributed to the decrease in the proportion of correctly selected solutions as parents from which offspring solutions are generated. The proportion of solutions correctly selected as parents can be increased by conducting additional replications for each solution. However, experimental evaluation suggests that a very high proportion of correctly selected solutions as parents is not required. A proportion of correctly selected solutions of around 0.75 seems sufficient for evolution strategies to perform adequately. Integrating statistical techniques into the algorithm's selection process does help evolution strategies cope with high levels of noise. There are four categories of techniques: statistical ranking and selection techniques, multiple comparison procedures, clustering techniques, and other techniques. Experimental comparison of indifference zone selection procedure by Dudewicz and Dalal (1975), sequential procedure by Kim and Nelson (2001), Tukey's Procedure, clustering procedure by Calsinki and Corsten (1985), and Scheffe's procedure (1985) under similar conditions suggests that the sequential ranking and selection procedure by Kim and Nelson (2001) helps evolution strategies cope with noise using the smallest number of replications. However, all of the techniques required a rather large number of replications, which suggests that better methods are needed. Experimental results also indicate that a statistical procedure is especially required during the later generations when solutions are spaced closely together in the search space (response surface).
Download or read book Aerospace System Analysis and Optimization in Uncertainty written by Loïc Brevault and published by Springer Nature. This book was released on 2020-08-26 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spotlighting the field of Multidisciplinary Design Optimization (MDO), this book illustrates and implements state-of-the-art methodologies within the complex process of aerospace system design under uncertainties. The book provides approaches to integrating a multitude of components and constraints with the ultimate goal of reducing design cycles. Insights on a vast assortment of problems are provided, including discipline modeling, sensitivity analysis, uncertainty propagation, reliability analysis, and global multidisciplinary optimization. The extensive range of topics covered include areas of current open research. This Work is destined to become a fundamental reference for aerospace systems engineers, researchers, as well as for practitioners and engineers working in areas of optimization and uncertainty. Part I is largely comprised of fundamentals. Part II presents methodologies for single discipline problems with a review of existing uncertainty propagation, reliability analysis, and optimization techniques. Part III is dedicated to the uncertainty-based MDO and related issues. Part IV deals with three MDO related issues: the multifidelity, the multi-objective optimization and the mixed continuous/discrete optimization and Part V is devoted to test cases for aerospace vehicle design.
Download or read book Parallel Problem Solving from Nature PPSN VIII written by Xin Yao and published by Springer Science & Business Media. This book was released on 2004-09-13 with total page 1204 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Foundations of Genetic Algorithms written by Alden H. Wright and published by Springer Science & Business Media. This book was released on 2005-07 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th workshop on the foundations of genetic algorithms, FOGA 2005, held in Aizu-Wakamatsu City, Japan, in January 2005. The 16 revised full papers presented provide an outstanding source of reference for the field of theoretical evolutionary computation including evolution strategies, evolutionary programming, and genetic programming, as well as the continuing growth in interactions with other fields such as mathematics, physics, and biology.
Download or read book Parallel Problem Solving from Nature PPSN XIV written by Julia Handl and published by Springer. This book was released on 2016-08-30 with total page 1033 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016, held in Edinburgh, UK, in September 2016. The total of 93 revised full papers were carefully reviewed and selected from 224 submissions. The meeting began with four workshops which offered an ideal opportunity to explore specific topics in intelligent transportation Workshop, landscape-aware heuristic search, natural computing in scheduling and timetabling, and advances in multi-modal optimization. PPSN XIV also included sixteen free tutorials to give us all the opportunity to learn about new aspects: gray box optimization in theory; theory of evolutionary computation; graph-based and cartesian genetic programming; theory of parallel evolutionary algorithms; promoting diversity in evolutionary optimization: why and how; evolutionary multi-objective optimization; intelligent systems for smart cities; advances on multi-modal optimization; evolutionary computation in cryptography; evolutionary robotics - a practical guide to experiment with real hardware; evolutionary algorithms and hyper-heuristics; a bridge between optimization over manifolds and evolutionary computation; implementing evolutionary algorithms in the cloud; the attainment function approach to performance evaluation in EMO; runtime analysis of evolutionary algorithms: basic introduction; meta-model assisted (evolutionary) optimization. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; differential evolution and swarm intelligence; dynamic, uncertain and constrained environments; genetic programming; multi-objective, many-objective and multi-level optimization; parallel algorithms and hardware issues; real-word applications and modeling; theory; diversity and landscape analysis.
Download or read book Recent Advances In Simulated Evolution And Learning written by Kay Chen Tan and published by World Scientific. This book was released on 2004-08-26 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: Inspired by the Darwinian framework of evolution through natural selection and adaptation, the field of evolutionary computation has been growing very rapidly, and is today involved in many diverse application areas. This book covers the latest advances in the theories, algorithms, and applications of simulated evolution and learning techniques. It provides insights into different evolutionary computation techniques and their applications in domains such as scheduling, control and power, robotics, signal processing, and bioinformatics. The book will be of significant value to all postgraduates, research scientists and practitioners dealing with evolutionary computation or complex real-world problems.This book has been selected for coverage in:• Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings)• CC Proceedings — Engineering & Physical Sciences
Download or read book Evolutionary Multi criterion Optimization written by Eckart Zitzler and published by Springer Science & Business Media. This book was released on 2001-02-28 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Conference on Multi-Criterion Optimization, EMO 2001, held in Zurich, Switzerland in March 2001. The 45 revised full papers presented were carefully reviewed and selected from a total of 87 submissions. Also included are two tutorial surveys and two invited papers. The book is organized in topical sections on algorithm improvements, performance assessment and comparison, constraint handling and problem decomposition, uncertainty and noise, hybrid and alternative methods, scheduling, and applications of multi-objective optimization in a variety of fields.
Download or read book Artificial Evolution written by Pierrick Legrand and published by Springer. This book was released on 2014-10-24 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th International Conference on Artificial Evolution, EA 2013, held in Bordeaux, France, in October 2013. The 20 revised papers were carefully reviewed and selected from 39 submissions. The papers are focused to theory, ant colony optimization, applications, combinatorial and discrete optimization, memetic algorithms, genetic programming, interactive evolution, parallel evolutionary algorithms, and swarm intelligence.
Download or read book Parallel Problem Solving from Nature PPSN XIII written by Thomas Bartz-Beielstein and published by Springer. This book was released on 2014-09-11 with total page 977 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Parallel Problem Solving from Nature, PPSN 2013, held in Ljubljana, Slovenia, in September 2014. The total of 90 revised full papers were carefully reviewed and selected from 217 submissions. The meeting began with 7 workshops which offered an ideal opportunity to explore specific topics in evolutionary computation, bio-inspired computing and metaheuristics. PPSN XIII also included 9 tutorials. The papers are organized in topical sections on adaption, self-adaption and parameter tuning; classifier system, differential evolution and swarm intelligence; coevolution and artificial immune systems; constraint handling; dynamic and uncertain environments; estimation of distribution algorithms and metamodelling; genetic programming; multi-objective optimisation; parallel algorithms and hardware implementations; real world applications; and theory.
Download or read book Parallel Problem Solving from Nature PPSN X written by Günter Rudolph and published by Springer. This book was released on 2008-09-16 with total page 1183 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Foundations of Genetic Algorithms written by Christopher R. Stephens and published by Springer Science & Business Media. This book was released on 2007-06-29 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 9th Workshop on the Foundations of Genetic Algorithms, FOGA 2007, held in Mexico City, Mexico in January 2007. The 11 revised full papers presented were carefully reviewed and selected during two rounds of reviewing and improvement from 22 submissions. The papers address all current topics in the field of theoretical evolutionary computation including evolution strategies, evolutionary programming, and genetic programming, and also depict the continuing growth in interactions with other fields such as mathematics, physics, and biology.
Download or read book Intelligent Data Engineering and Automated Learning written by Jiming Liu and published by Springer Science & Business Media. This book was released on 2003-07-29 with total page 1161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the throughly refereed post-proceedings of the 4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003, held in Hong Kong, China in March 2003. The 164 revised papers presented were carefully reviewed and selected from 321 submissions; for inclusion in this post-proceedings another round of revision was imposed. The papers are organized in topical sections an agents, automated learning, bioinformatics, data mining, multimedia information, and financial engineering.
