Ebook: Handbook of Memetic Algorithms

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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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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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.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Basic Concepts....Pages 3-7
Evolutionary Algorithms....Pages 9-27
Local Search....Pages 29-41
A Primer on Memetic Algorithms....Pages 43-52
Front Matter....Pages 53-53
Parametrization and Balancing Local and Global Search....Pages 55-72
Memetic Algorithms in Discrete Optimization....Pages 73-94
Memetic Algorithms and Fitness Landscapes in Combinatorial Optimization....Pages 95-119
Memetic Algorithms in Continuous Optimization....Pages 121-134
Memetic Algorithms in Constrained Optimization....Pages 135-151
Diversity Management in Memetic Algorithms....Pages 153-165
Self-adaptative and Coevolving Memetic Algorithms....Pages 167-188
Memetic Algorithms and Complete Techniques....Pages 189-200
Multiobjective Memetic Algorithms....Pages 201-217
Memetic Algorithms in the Presence of Uncertainties....Pages 219-237
Front Matter....Pages 239-239
Memetic Algorithms in Engineering and Design....Pages 241-260
Memetic Algorithms in Bioinformatics....Pages 261-271
Front Matter....Pages 273-273
Memetic Algorithms: The Untold Story....Pages 275-309
Back Matter....Pages -

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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.

Content:
Front Matter....Pages -
Front Matter....Pages 1-1
Basic Concepts....Pages 3-7
Evolutionary Algorithms....Pages 9-27
Local Search....Pages 29-41
A Primer on Memetic Algorithms....Pages 43-52
Front Matter....Pages 53-53
Parametrization and Balancing Local and Global Search....Pages 55-72
Memetic Algorithms in Discrete Optimization....Pages 73-94
Memetic Algorithms and Fitness Landscapes in Combinatorial Optimization....Pages 95-119
Memetic Algorithms in Continuous Optimization....Pages 121-134
Memetic Algorithms in Constrained Optimization....Pages 135-151
Diversity Management in Memetic Algorithms....Pages 153-165
Self-adaptative and Coevolving Memetic Algorithms....Pages 167-188
Memetic Algorithms and Complete Techniques....Pages 189-200
Multiobjective Memetic Algorithms....Pages 201-217
Memetic Algorithms in the Presence of Uncertainties....Pages 219-237
Front Matter....Pages 239-239
Memetic Algorithms in Engineering and Design....Pages 241-260
Memetic Algorithms in Bioinformatics....Pages 261-271
Front Matter....Pages 273-273
Memetic Algorithms: The Untold Story....Pages 275-309
Back Matter....Pages -
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