Ebook: Evolutionary Optimization

Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization techniques. However, most papers on the application of evolutionary computation techniques to Operations Research /Management Science (OR/MS) problems have scattered around in different journals and conference proceedings. They also tend to focus on a very special and narrow topic. It is the right time that an archival book series publishes a special volume which - cludes critical reviews of the state-of-art of those evolutionary com- tation techniques which have been found particularly useful for OR/MS problems, and a collection of papers which represent the latest devel- ment in tackling various OR/MS problems by evolutionary computation techniques. This special volume of the book series on Evolutionary - timization aims at filling in this gap in the current literature. The special volume consists of invited papers written by leading - searchers in the field. All papers were peer reviewed by at least two recognised reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.

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The use of evolutionary computation techniques has grown considerably over the past several years. Over this time, the use and applications of these techniques have been further enhanced resulting in a set of computational intelligence (also known as modern heuristics) tools that are particularly adept for solving complex optimization problems. Moreover, they are characteristically more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. Hence, evolutionary computation techniques have dealt with complex optimization problems better than traditional optimization techniques although they can be applied to easy and simple problems where conventional techniques work well. Clearly there is a need for a volume that both reviews state-of-the-art evolutionary computation techniques, and surveys the most recent developments in their use for solving complex OR/MS problems. This volume on Evolutionary Optimization seeks to fill this need.

Evolutionary Optimization is a volume of invited papers written by leading researchers in the field. All papers were peer reviewed by at least two recognized reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.

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The use of evolutionary computation techniques has grown considerably over the past several years. Over this time, the use and applications of these techniques have been further enhanced resulting in a set of computational intelligence (also known as modern heuristics) tools that are particularly adept for solving complex optimization problems. Moreover, they are characteristically more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. Hence, evolutionary computation techniques have dealt with complex optimization problems better than traditional optimization techniques although they can be applied to easy and simple problems where conventional techniques work well. Clearly there is a need for a volume that both reviews state-of-the-art evolutionary computation techniques, and surveys the most recent developments in their use for solving complex OR/MS problems. This volume on Evolutionary Optimization seeks to fill this need.

Evolutionary Optimization is a volume of invited papers written by leading researchers in the field. All papers were peer reviewed by at least two recognized reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.

Content:
Front Matter....Pages i-xiv
Front Matter....Pages 1-1
Conventional Optimization Techniques....Pages 3-25
Evolutionary Computation....Pages 27-53
Front Matter....Pages 55-55
Evolutionary Algorithms and Constrained Optimization....Pages 57-86
Constrained Evolutionary Optimization....Pages 87-113
Front Matter....Pages 115-115
Evolutionary Multi-Objective Optimization: A Critical Review....Pages 117-146
Multi-Objective Evolutionary Algorithms for Engineering Shape Design....Pages 147-175
Assessment Methodologies for Multiobjective Evolutionary Algorithms....Pages 177-195
Front Matter....Pages 197-197
Utilizing Hybrid Genetic Algorithms....Pages 199-228
Using Evolutionary Algorithms to Solve Problems by Combining Choices of Heuristics....Pages 229-252
Constrained Genetic Algorithms and Their Applications in Nonlinear Constrained Optimization....Pages 253-275
Front Matter....Pages 277-277
Parameter Selection....Pages 279-306
Front Matter....Pages 307-307
Design of Production Facilities Using Evolutionary Computing....Pages 309-327
Virtual Population and Acceleration Techniques for Evolutionary Power Flow Calculation in Power Systems....Pages 329-345
Front Matter....Pages 347-347
Methods for the Analysis of Evolutionary Algorithms on Pseudo-Boolean Functions....Pages 349-369
A Genetic Algorithm Heuristic for Finite Horizon Partially Observed Markov Decision Problems....Pages 371-398
Using Genetic Algorithms to Find Good K-Tree Subgraphs....Pages 399-413
Back Matter....Pages 415-418

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The use of evolutionary computation techniques has grown considerably over the past several years. Over this time, the use and applications of these techniques have been further enhanced resulting in a set of computational intelligence (also known as modern heuristics) tools that are particularly adept for solving complex optimization problems. Moreover, they are characteristically more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. Hence, evolutionary computation techniques have dealt with complex optimization problems better than traditional optimization techniques although they can be applied to easy and simple problems where conventional techniques work well. Clearly there is a need for a volume that both reviews state-of-the-art evolutionary computation techniques, and surveys the most recent developments in their use for solving complex OR/MS problems. This volume on Evolutionary Optimization seeks to fill this need.

Evolutionary Optimization is a volume of invited papers written by leading researchers in the field. All papers were peer reviewed by at least two recognized reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.

Content:
Front Matter....Pages i-xiv
Front Matter....Pages 1-1
Conventional Optimization Techniques....Pages 3-25
Evolutionary Computation....Pages 27-53
Front Matter....Pages 55-55
Evolutionary Algorithms and Constrained Optimization....Pages 57-86
Constrained Evolutionary Optimization....Pages 87-113
Front Matter....Pages 115-115
Evolutionary Multi-Objective Optimization: A Critical Review....Pages 117-146
Multi-Objective Evolutionary Algorithms for Engineering Shape Design....Pages 147-175
Assessment Methodologies for Multiobjective Evolutionary Algorithms....Pages 177-195
Front Matter....Pages 197-197
Utilizing Hybrid Genetic Algorithms....Pages 199-228
Using Evolutionary Algorithms to Solve Problems by Combining Choices of Heuristics....Pages 229-252
Constrained Genetic Algorithms and Their Applications in Nonlinear Constrained Optimization....Pages 253-275
Front Matter....Pages 277-277
Parameter Selection....Pages 279-306
Front Matter....Pages 307-307
Design of Production Facilities Using Evolutionary Computing....Pages 309-327
Virtual Population and Acceleration Techniques for Evolutionary Power Flow Calculation in Power Systems....Pages 329-345
Front Matter....Pages 347-347
Methods for the Analysis of Evolutionary Algorithms on Pseudo-Boolean Functions....Pages 349-369
A Genetic Algorithm Heuristic for Finite Horizon Partially Observed Markov Decision Problems....Pages 371-398
Using Genetic Algorithms to Find Good K-Tree Subgraphs....Pages 399-413
Back Matter....Pages 415-418
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