From the reviews: "The book contains 17 chapters written by leading experts in evolutionary computation. ... Of special value is the analysis of evolutionary algorithms on pseudo-Boolean functions, given by Ingo Wegener. He and his coauthors are the first, who proved substantially sharp results on the expected run time and the success probability for evolutionary algorithms with (respectively without) crossover, giving sharp upper and lower bounds." (Hartmut Noltemeier, Zentralblatt MATH, Vol. 1072 (23), 2005)

• Conventional Optimization Techniques.- Evolutionary Computation.- Single Objective Optimization.- Evolutionary Algorithms and Constrained Optimization.- Constrained Evolutionary Optimization.- Multi-Objective Optimization.- Evolutionary Multi-Objective Optimization: A Critical Review.- Multi-Objective Evolutionary Algorithms for Engineering Shape Design.- Assessment Methodologies for Multiobjective Evolutionary Algorithms.- Hybrid Algorithms.- Utilizing Hybrid Genetic Algorithms.- Using Evolutionary Algorithms to Solve Problems by Combining Choices of Heuristics.- Constrained Genetic Algorithms and Their Applications in Nonlinear Constrained Optimization.- Parameter Selection in EAs.- Parameter Selection.- Application of EAs to Practical Problems.- Design of Production Facilities Using Evolutionary Computing.- Virtual Population and Acceleration Techniques for Evolutionary Power Flow Calculation in Power Systems.- Application of EAs to Theoretical Problems.- Methods for the Analysis of Evolutionary Algorithms on Pseudo-Boolean Functions.- A Genetic Algorithm Heuristic for Finite Horizon Partially Observed Markov Decision Problems.- Using Genetic Algorithms to Find Good K-Tree Subgraphs.

From the reviews: "The book contains 17 chapters written by leading experts in evolutionary computation. ... Of special value is the analysis of evolutionary algorithms on pseudo-Boolean functions, given by Ingo Wegener. He and his coauthors are the first, who proved substantially sharp results on the expected run time and the success probability for evolutionary algorithms with (respectively without) crossover, giving sharp upper and lower bounds." (Hartmut Noltemeier, Zentralblatt MATH, Vol. 1072 (23), 2005)

• Conventional Optimization Techniques.- Evolutionary Computation.- Single Objective Optimization.- Evolutionary Algorithms and Constrained Optimization.- Constrained Evolutionary Optimization.- Multi-Objective Optimization.- Evolutionary Multi-Objective Optimization: A Critical Review.- Multi-Objective Evolutionary Algorithms for Engineering Shape Design.- Assessment Methodologies for Multiobjective Evolutionary Algorithms.- Hybrid Algorithms.- Utilizing Hybrid Genetic Algorithms.- Using Evolutionary Algorithms to Solve Problems by Combining Choices of Heuristics.- Constrained Genetic Algorithms and Their Applications in Nonlinear Constrained Optimization.- Parameter Selection in EAs.- Parameter Selection.- Application of EAs to Practical Problems.- Design of Production Facilities Using Evolutionary Computing.- Virtual Population and Acceleration Techniques for Evolutionary Power Flow Calculation in Power Systems.- Application of EAs to Theoretical Problems.- Methods for the Analysis of Evolutionary Algorithms on Pseudo-Boolean Functions.- A Genetic Algorithm Heuristic for Finite Horizon Partially Observed Markov Decision Problems.- Using Genetic Algorithms to Find Good K-Tree Subgraphs.

From the reviews: "The book contains 17 chapters written by leading experts in evolutionary computation. ... Of special value is the analysis of evolutionary algorithms on pseudo-Boolean functions, given by Ingo Wegener. He and his coauthors are the first, who proved substantially sharp results on the expected run time and the success probability for evolutionary algorithms with (respectively without) crossover, giving sharp upper and lower bounds." (Hartmut Noltemeier, Zentralblatt MATH, Vol. 1072 (23), 2005)

• Conventional Optimization Techniques.- Evolutionary Computation.- Single Objective Optimization.- Evolutionary Algorithms and Constrained Optimization.- Constrained Evolutionary Optimization.- Multi-Objective Optimization.- Evolutionary Multi-Objective Optimization: A Critical Review.- Multi-Objective Evolutionary Algorithms for Engineering Shape Design.- Assessment Methodologies for Multiobjective Evolutionary Algorithms.- Hybrid Algorithms.- Utilizing Hybrid Genetic Algorithms.- Using Evolutionary Algorithms to Solve Problems by Combining Choices of Heuristics.- Constrained Genetic Algorithms and Their Applications in Nonlinear Constrained Optimization.- Parameter Selection in EAs.- Parameter Selection.- Application of EAs to Practical Problems.- Design of Production Facilities Using Evolutionary Computing.- Virtual Population and Acceleration Techniques for Evolutionary Power Flow Calculation in Power Systems.- Application of EAs to Theoretical Problems.- Methods for the Analysis of Evolutionary Algorithms on Pseudo-Boolean Functions.- A Genetic Algorithm Heuristic for Finite Horizon Partially Observed Markov Decision Problems.- Using Genetic Algorithms to Find Good K-Tree Subgraphs.