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Download the fantastic book titled Theory of Evolutionary Computation written by Benjamin Doerr, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Theory of Evolutionary Computation", which was released on 20 November 2019. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.

Summary of Theory of Evolutionary Computation by Benjamin Doerr PDF

This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influences performance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming. Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing.


Detail About Theory of Evolutionary Computation PDF

  • Author : Benjamin Doerr
  • Publisher : Springer Nature
  • Genre : Computers
  • Total Pages : 506 pages
  • ISBN : 3030294145
  • PDF File Size : 11,5 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Theory of Evolutionary Computation

Theory of Evolutionary Computation
  • Publisher : Springer Nature
  • File Size : 49,8 Mb
  • Release Date : 20 November 2019
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This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. It starts with two chapters on mathematical methods

Introduction to Evolutionary Computing

Introduction to Evolutionary Computing
  • Publisher : Springer Science & Business Media
  • File Size : 20,5 Mb
  • Release Date : 14 March 2013
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The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The

Theoretical Aspects of Evolutionary Computing

Theoretical Aspects of Evolutionary Computing
  • Publisher : Springer Science & Business Media
  • File Size : 33,5 Mb
  • Release Date : 08 May 2001
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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

Advances in Evolutionary Computing

Advances in Evolutionary Computing
  • Publisher : Springer Science & Business Media
  • File Size : 34,8 Mb
  • Release Date : 06 December 2012
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This book provides a collection of fourty articles containing new material on both theoretical aspects of Evolutionary Computing (EC), and demonstrating the usefulness/success of it for various kinds of

Evolutionary Computation: Theory and Applications

Evolutionary Computation: Theory and Applications
  • Publisher : World Scientific
  • File Size : 30,5 Mb
  • Release Date : 22 November 1999
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Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary

Evolutionary Computation for Modeling and Optimization

Evolutionary Computation for Modeling and Optimization
  • Publisher : Springer Science & Business Media
  • File Size : 24,9 Mb
  • Release Date : 04 April 2006
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Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.

Fuzzy Evolutionary Computation

Fuzzy Evolutionary Computation
  • Publisher : Springer Science & Business Media
  • File Size : 34,6 Mb
  • Release Date : 06 December 2012
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As of today, Evolutionary Computing and Fuzzy Set Computing are two mature, wen -developed, and higbly advanced technologies of information processing. Bach of them has its own clearly defined research

The Theory of Evolution Strategies

The Theory of Evolution Strategies
  • Publisher : Springer Science & Business Media
  • File Size : 49,6 Mb
  • Release Date : 09 March 2013
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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

Frontiers of Evolutionary Computation

Frontiers of Evolutionary Computation
  • Publisher : Springer Science & Business Media
  • File Size : 44,7 Mb
  • Release Date : 11 April 2006
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Frontiers of Evolutionary Computation brings together eleven contributions by international leading researchers discussing what significant issues still remain unresolved in the field of Evolutionary Computation (Ee. They explore such topics

Evolutionary Computation

Evolutionary Computation
  • Publisher : MIT Press
  • File Size : 51,5 Mb
  • Release Date : 03 February 2006
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This text is an introduction to the field of evolutionary computation. It approaches evolution strategies and genetic programming, as instances of a more general class of evolutionary algorithms.