Advances in Evolutionary Algorithms

Advances in Evolutionary Algorithms
Author: Chang Wook Ahn
Publisher: Springer
Total Pages: 180
Release: 2007-05-22
Genre: Technology & Engineering
ISBN: 3540317597

Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. This book provides effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms.

Genetic Programming Theory and Practice XVII

Genetic Programming Theory and Practice XVII
Author: Wolfgang Banzhaf
Publisher: Springer Nature
Total Pages: 409
Release: 2020-05-07
Genre: Computers
ISBN: 3030399583

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. In this year’s edition, the topics covered include many of the most important issues and research questions in the field, such as: opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms.The volume includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

Parallel Genetic Algorithms

Parallel Genetic Algorithms
Author: Gabriel Luque
Publisher: Springer Science & Business Media
Total Pages: 173
Release: 2011-06-15
Genre: Computers
ISBN: 3642220835

This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers can learn how to solve complex tasks by reducing their high computational times. Dealing with two scientific fields (parallelism and GAs) is always difficult, and the book seeks at gracefully introducing from basic concepts to advanced topics. The presentation is structured in three parts. The first one is targeted to the algorithms themselves, discussing their components, the physical parallelism, and best practices in using and evaluating them. A second part deals with the theory for pGAs, with an eye on theory-to-practice issues. A final third part offers a very wide study of pGAs as practical problem solvers, addressing domains such as natural language processing, circuits design, scheduling, and genomics. This volume will be helpful both for researchers and practitioners. The first part shows pGAs to either beginners and mature researchers looking for a unified view of the two fields: GAs and parallelism. The second part partially solves (and also opens) new investigation lines in theory of pGAs. The third part can be accessed independently for readers interested in applications. The result is an excellent source of information on the state of the art and future developments in parallel GAs.

Evolutionary Algorithms in Theory and Practice

Evolutionary Algorithms in Theory and Practice
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.

Evolutionary Algorithms for Solving Multi-Objective Problems

Evolutionary Algorithms for Solving Multi-Objective Problems
Author: Carlos Coello Coello
Publisher: Springer Science & Business Media
Total Pages: 810
Release: 2007-08-26
Genre: Computers
ISBN: 0387367977

This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.

Evolutionary Algorithms in Theory and Practice

Evolutionary Algorithms in Theory and Practice
Author: Thomas Bäck
Publisher: Oxford University Press, USA
Total Pages: 329
Release: 1996
Genre: Computers
ISBN: 0195099710

A comparison of evolutionary algorithms. Organic evolution and problem solving. Biological background. Evolutionary algorithms and artificial intelligence. Evolutionary algorithms and global optimization. Early approaches. Specific evolutionary algorithms. Evolution strategies. Evolutionary programming. Genetic algorithms. Artificial landscapes. An empirical comparison. Extending genetic algorithms. Selection. Selection mechanisms. Experimental investigation of selection. Mutation. Simplified genetic algorithms. An experiment in meta-evolution. Summary and outlook. Data for the fletcher-powell function. Data from selection experiments. Software. The multiprocessor environment; mathematical symbols.

The Theory of Evolution Strategies

The Theory of Evolution Strategies
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.

Genetic Programming Theory and Practice II

Genetic Programming Theory and Practice II
Author: Una-May O'Reilly
Publisher: Springer Science & Business Media
Total Pages: 330
Release: 2006-03-16
Genre: Computers
ISBN: 0387232540

The work described in this book was first presented at the Second Workshop on Genetic Programming, Theory and Practice, organized by the Center for the Study of Complex Systems at the University of Michigan, Ann Arbor, 13-15 May 2004. The goal of this workshop series is to promote the exchange of research results and ideas between those who focus on Genetic Programming (GP) theory and those who focus on the application of GP to various re- world problems. In order to facilitate these interactions, the number of talks and participants was small and the time for discussion was large. Further, participants were asked to review each other's chapters before the workshop. Those reviewer comments, as well as discussion at the workshop, are reflected in the chapters presented in this book. Additional information about the workshop, addendums to chapters, and a site for continuing discussions by participants and by others can be found at http://cscs.umich.edu:8000/GPTP-20041. We thank all the workshop participants for making the workshop an exciting and productive three days. In particular we thank all the authors, without whose hard work and creative talents, neither the workshop nor the book would be possible. We also thank our keynote speakers Lawrence ("Dave") Davis of NuTech Solutions, Inc., Jordan Pollack of Brandeis University, and Richard Lenski of Michigan State University, who delivered three thought-provoking speeches that inspired a great deal of discussion among the participants.

Evolutionary Learning: Advances in Theories and Algorithms

Evolutionary Learning: Advances in Theories and Algorithms
Author: Zhi-Hua Zhou
Publisher: Springer
Total Pages: 361
Release: 2019-05-22
Genre: Computers
ISBN: 9811359563

Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.

Proceedings of the First International Conference on Genetic Algorithms and their Applications

Proceedings of the First International Conference on Genetic Algorithms and their Applications
Author: John J. Grefenstette
Publisher: Psychology Press
Total Pages: 345
Release: 2014-01-02
Genre: Psychology
ISBN: 1317760247

Computer solutions to many difficult problems in science and engineering require the use of automatic search methods that consider a large number of possible solutions to the given problems. This book describes recent advances in the theory and practice of one such search method, called Genetic Algorithms. Genetic algorithms are evolutionary search techniques based on principles derived from natural population genetics, and are currently being applied to a variety of difficult problems in science, engineering, and artificial intelligence.