The Theory Of Evolution Strategies
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| Author | : Hans-Georg Beyer |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 393 |
| Release | : 2013-03-09 |
| Genre | : Computers |
| ISBN | : 3662043785 |
Evolutionary algorithms, such as evolution strategies, genetic algorithms, or evolutionary programming, have found broad acceptance in the last ten years. In contrast to its broad propagation, theoretical analysis in this subject 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 deriving a qualitative understanding of why and how these ES algorithms work.
| Author | : John Maynard Smith |
| Publisher | : Cambridge University Press |
| Total Pages | : 244 |
| Release | : 1982-10-21 |
| Genre | : Science |
| ISBN | : 9780521288842 |
This 1982 book is an account of an alternative way of thinking about evolution and the theory of games.
| Author | : H.W. Bergmann |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 1989-05-26 |
| Genre | : Technology & Engineering |
| ISBN | : 9783540512578 |
Proceedings of an International Seminar Organized by Deutsche Forschungsanstalt für Luft- und Raumfahrt (DLR) Bonn, June 1989
| Author | : Thomas Back |
| Publisher | : Oxford University Press |
| Total Pages | : 329 |
| Release | : 1996-01-11 |
| Genre | : Computers |
| ISBN | : 0195356705 |
This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.
| Author | : Leila Kallel |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 516 |
| Release | : 2001-05-08 |
| Genre | : Business & Economics |
| ISBN | : 9783540673965 |
This book is the first in the field to provide extensive, entry level tutorials to the theory of Evolutionary Computing, covering the main approaches to understanding the dynamics of Evolutionary Algorithms. It combines this with recent, previously unpublished research papers based on the material of the tutorials. The outcome is a book which is self-contained to a large degree, attractive both to graduate students and researchers from other fields who want to get acquainted with the theory of Evolutionary Computing, and to active researchers in the field who can use this book as a reference and a source of recent results.
| Author | : Dirk V. Arnold |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2012-10-24 |
| Genre | : Computers |
| ISBN | : 9781461353973 |
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.
| Author | : Robert Axelrod |
| Publisher | : Basic Books |
| Total Pages | : 258 |
| Release | : 2009-04-29 |
| Genre | : Business & Economics |
| ISBN | : 0786734884 |
A famed political scientist's classic argument for a more cooperative world We assume that, in a world ruled by natural selection, selfishness pays. So why cooperate? In The Evolution of Cooperation, political scientist Robert Axelrod seeks to answer this question. In 1980, he organized the famed Computer Prisoners Dilemma Tournament, which sought to find the optimal strategy for survival in a particular game. Over and over, the simplest strategy, a cooperative program called Tit for Tat, shut out the competition. In other words, cooperation, not unfettered competition, turns out to be our best chance for survival. A vital book for leaders and decision makers, The Evolution of Cooperation reveals how cooperative principles help us think better about everything from military strategy, to political elections, to family dynamics.
| Author | : Beatrice Heuser |
| Publisher | : Cambridge University Press |
| Total Pages | : |
| Release | : 2010-10-14 |
| Genre | : Political Science |
| ISBN | : 113949256X |
Is there a 'Western way of war' which pursues battles of annihilation and single-minded military victory? Is warfare on a path to ever greater destructive force? This magisterial account answers these questions by tracing the history of Western thinking about strategy - the employment of military force as a political instrument - from antiquity to the present day. Assessing sources from Vegetius to contemporary America, and with a particular focus on strategy since the Napoleonic Wars, Beatrice Heuser explores the evolution of strategic thought, the social institutions, norms and patterns of behaviour within which it operates, the policies that guide it and the cultures that influence it. Ranging across technology and warfare, total warfare and small wars as well as land, sea, air and nuclear warfare, she demonstrates that warfare and strategic thinking have fluctuated wildly in their aims, intensity, limitations and excesses over the past two millennia.
| Author | : Rafael Martí |
| Publisher | : Springer |
| Total Pages | : 3000 |
| Release | : 2017-01-16 |
| Genre | : Computers |
| ISBN | : 9783319071237 |
Heuristics are strategies using readily accessible, loosely applicable information to control problem solving. Algorithms, for example, are a type of heuristic. By contrast, Metaheuristics are methods used to design Heuristics and may coordinate the usage of several Heuristics toward the formulation of a single method. GRASP (Greedy Randomized Adaptive Search Procedures) is an example of a Metaheuristic. To the layman, heuristics may be thought of as ‘rules of thumb’ but despite its imprecision, heuristics is a very rich field that refers to experience-based techniques for problem-solving, learning, and discovery. Any given solution/heuristic is not guaranteed to be optimal but heuristic methodologies are used to speed up the process of finding satisfactory solutions where optimal solutions are impractical. The introduction to this Handbook provides an overview of the history of Heuristics along with main issues regarding the methodologies covered. This is followed by Chapters containing various examples of local searches, search strategies and Metaheuristics, leading to an analyses of Heuristics and search algorithms. The reference concludes with numerous illustrations of the highly applicable nature and implementation of Heuristics in our daily life. Each chapter of this work includes an abstract/introduction with a short description of the methodology. Key words are also necessary as part of top-matter to each chapter to enable maximum search engine optimization. Next, chapters will include discussion of the adaptation of this methodology to solve a difficult optimization problem, and experiments on a set of representative problems.
| Author | : Jose A. Lozano |
| Publisher | : Springer |
| Total Pages | : 306 |
| Release | : 2006-01-21 |
| Genre | : Technology & Engineering |
| ISBN | : 3540324941 |
Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field. This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.
