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Operations Research/Computer Science Interfaces #42: Cellular Genetic Algorithmsby Enrique Alba
Synopses & Reviews
CELLULAR GENETIC ALGORITHMS defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications. These methods can include local search (memetic algorithms), cooperation, parallelism, multi-objective, estimations of distributions, and self-adaptive ideas to extend their applicability. The methods are benchmarked against well-known metaheutistics like Genetic Algorithms, Tabu Search, heterogeneous GAs, Estimation of Distribution Algorithms, etc. Also, a publicly available software tool is offered to reduce the learning curve in applying these techniques. The three final chapters will use the classic problem of "vehicle routing" and the hot topics of "ad-hoc mobile networks" and "DNA genome sequencing" to clearly illustrate and demonstrate the power and utility of these algorithms.
Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book with equal and parallel emphasis on both theory and practice. This book is a key source for studying and designing cellular GAs, as well as a self-contained primary reference book for these algorithms.
Table of Contents
Introduction to cellular GAs.- Panmictic GAs: cGAs versus steady-state and generational GAs.- Structured GAs: cGAs versus distributed GAs.- Using the ratio concept to define efficient cGAs.- New models with dynamic population shape in cGAs.- Multi-objective cGAs.- Memetic cGAs.- Theory of convergence for cGAs.- cGAs versus other metaheuristics.- cGAs for discrete optimization.- cGAs for continuous optimization.- cGAs for the vehicle routing problem.- cGAs for MANETS (mobile ad-hoc networks).- Index.
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