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Handbook of Memetic AlgorithmsNovember 2011

November 2011Read More

Authors:
Ferrante Neri,
Carlos Cotta,
Pablo Moscato

Publisher:

Springer Publishing Company, Incorporated

ISBN:978-3-642-23246-6

Published:02 November 2011

Pages:

396

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Handbook of Memetic Algorithms

2011

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Abstract

Memetic Algorithms (MAs) are computational intelligence structures combining multiple and various operators in order to address optimization problems. The combination and interaction amongst operators evolves and promotes the diffusion of the most successful units and generates an algorithmic behavior which can handle complex objective functions and hard fitness landscapes. Handbook of Memetic Algorithms organizes, in a structured way, all the the most important results in the field of MAs since their earliest definition until now. A broad review including various algorithmic solutions as well as successful applications is included in this book. Each class of optimization problems, such as constrained optimization, multi-objective optimization, continuous vs combinatorial problems, uncertainties, are analysed separately and, for each problem, memetic recipes for tackling the difficulties are given with some successful examples. Although this book contains chapters written by multiple authors, a great attention has been given by the editors to make it a compact and smooth work which covers all the main areas of computational intelligence optimization. It is not only a necessary read for researchers working in the research area, but also a useful handbook for practitioners and engineers who need to address real-world optimization problems. In addition, the book structure makes it an interesting work also for graduate students and researchers is related fields of mathematics and computer science.

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Contributors

Ferrante Neri

Nanjing University of Information Science & Technology
Carlos Cotta

University of Malaga
Pablo Moscato

The University of Newcastle, Australia

Index Terms

Handbook of Memetic Algorithms
1. General and reference
  1. Document types
    1. Reference works
2. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
    2. Numerical analysis
      1. Numerical differentiation
3. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

Reviews

Reviewer: Patrick Siarry

Memetic algorithms (MAs) belong to the class of metaheuristics aimed at solving "hard" optimization problems. MAs are based on the combination of multiple operators to tackle various types of optimization problems, discrete or continuous, mono or multiobjective, with or without constraints, static or dynamic, with or without uncertainties. This book emphasizes self-adaptive, coevolutionary, and diversity-adaptive schemes used for the automatic coordination of different algorithm components. At last, a set of successful examples in real-world engineering applications is presented. The handbook comprises 17 chapters, grouped into four parts: "Foundations," "Methodology," "Applications," and "Epilogue." In the first part (chapters 1 through 4), the basic concepts of MAs are introduced, with stress on two issues: the design and tuning of evolutionary algorithms, and the main structures of local search algorithms, both in continuous and combinatorial spaces. In the second part (chapters 5 through 14), methodological aspects of algorithmic design and the handling of problem difficulties are studied. The book focuses on the main issues related to MAs, particularly the balance of global and local search within evolutionary frameworks, adapting to discrete and combinatorial optimization problems, the design of semantic combination operators, the management of population diversity, and the handling of constraints. Advanced fitness landscape analysis techniques are also presented, together with a novel taxonomy of memetic approaches for continuous optimization. A chapter is devoted to diversity-based adaptive systems, including a detailed analysis of recently proposed diversity metrics for adaptive MAs. The authors clearly show how adaptive schemes containing local search information can lead to the design of flexible memetic frameworks. Another recent search avenue, the combination of MAs with exact techniques, is then discussed, which yields MAs capable of finding the true global optimum or at least guaranteeing approximation bounds. The central part of the book concludes with MA implementations for multiobjective optimization problems and for optimization problems in the presence of uncertainties. The third part of the book (chapters 15 and 16) gives several examples of applying MAs in real-life engineering problems and in bioinformatics. Finally, the last part (chapter 17) contains the epilogue, concerning the future development of the MA field. This handbook offers readers many fresh ideas on the difficult field of MAs. It is very well written and authored by well-known researchers in the field. It is easy to read, with a style that exploits many examples to illustrate some rather abstract concepts. It will be very useful both for researchers and for high-level students interested by trying to explain the success or failure of a memetic algorithm, when applied to a given optimization problem. The global coherence of the book could have been further improved with the addition of a conclusion, to recapitulate the main relevant contributions in the book. In the same spirit, the chapters seem to be presented in a random order, with no logical order underlined by the editors. However, these criticisms are minor. The book presents the state of the art of memetic algorithms in a structured, self-contained, and methodological way. Online Computing Reviews Service

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