Cellular Genetic 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. 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 metaheuristics 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.

Key source for studying and designing cellular GAs, as well as a self-contained primary reference book for these algorithms Throughout the book, there is an equal and parallel emphasis on both theory and practice Covers and provides results for both continuous and discrete problems - hence it's theoretical and application coverage is broad Explores both academic as well as real world problems, providing balance for researchers and practitioners Coverage includes multi-objective optimization, memetic extensions, and the relationship to new algorithms like EDAs, and high-interest-practical applications Includes supplementary material: sn.pub/extras

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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.

Contenu
I Introduction.- to Cellular Genetic Algorithms.- The State of the Art in Cellular Evolutionary Algorithms.- II Characterizing Cellular Genetic Algorithms.- On the Effects of Structuring the Population.- Some Theory: A Selection Pressure Study on cGAs.- III Algorithmic Models and Extensions.- Algorithmic and Experimental Design.- Design of Self-adaptive cGAs.- Design of Cellular Memetic Algorithms.- Design of Parallel Cellular Genetic Algorithms.- Designing Cellular Genetic Algorithms for Multi-objective Optimization.- Other Cellular Models.- Software for cGAs: The JCell Framework.- IV Applications of cGAs.- Continuous Optimization.- Logistics: The Vehicle Routing Problem.- Telecommunications: Optimization of the Broadcasting Process in MANETs.- Bioinformatics: The DNA Fragment Assembly Problem.