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• Evolutionary Multiobjective Optimization.- Recent Trends in Evolutionary Multiobjective Optimization.- Self-adaptation and Convergence of Multiobjective Evolutionary Algorithms in Continuous Search Spaces.- A Simple Approach to Evolutionary Multiobjective Optimization.- Quad-trees: A Data Structure for Storing Pareto-sets in Multiobjective Evolutionary Algorithms with Elitism.- Scalable Test Problems for Evolutionary Multiobjective Optimization.- Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for Multi-criteria Optimization Problems.- Evolving Continuous Pareto Regions.- MOGADES: Multiobjective Genetic Algorithm with Distributed Environment Scheme.- Use of Multiobjective Optimization Concepts to Handle Constraints in Genetic Algorithms.- Multi-criteria Optimization of Finite State Automata: Maximizing Performance while Minimizing Description Length.- Multiobjective Optimization of Space Structures under Static and Seismic Loading Conditions.
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"Evolutionary Multiobjective Optimization" is a rare collection of the latest state-of-the-art theoretical research, design challenges and applications in the field of multiobjective optimization paradigms using evolutionary algorithms. It includes two introductory chapters giving all the fundamental definitions, several complex test functions and a practical problem involving the multiobjective optimization of space structures under static and seismic loading conditions used to illustrate the various multiobjective optimization concepts. Important features include: detailed overview of all the multiobjective optimization paradigms using evolutionary algorithms; excellent coverage of timely, advanced multiobjective optimization topics; state-of-the-art theoretical research and application developments; and chapters authored by pioneers in the field. Academics and industrial scientists as well as engineers engaged in research, development and application of evolutionary algorithm based "Multiobjective Optimization" will find the comprehensive coverage of this book invaluable.
(source: Nielsen Book Data)

• Part I: Advances For New Model And Solution Approaches.- A Scatter Search Tutorial for Graph-Based Permutation Problems.- A Multistart Scatter Search Heuristic for Smooth NLP and MINLP Problems.- Scatter Search Methods for the Covering Tour Problem.- Solution for the Sonet Ring Assignment Problem with Capacity Constraints.- Part II: Advances for Solving Classical Problems.- A Very Fast Tabu Search Algorithm for Job Shop Problem.- Tabu Search Heuristics for the Vehicle Routing Problem.- Some New Ideas in TS for Job Shop Scheduling.- A Tabu Search Heuristic for the Uncapacitated Facility Location Problem.- Adaptive Memory Search Guidance for Satisfiability Problems.- Part III: Experimental Evaluation.- Lessons from Applying and Experimenting with Scatter Search.- Tabu Search for Mixed-Integer Programming.- Scatter Search vs. Genetic Algorithms: An experimental evaluation with permutation problems.- Part IV: Review of Recent Developments.- Parallel Computation, Co-operation, Tabu Search.- Using Group Theory to Construct and Characterize Metaheuristic Search Neighborhoods.- Logistics Management: An Opportunity for Metaheuristics.- Part V: New Procedural Designs.- On the Integration of Metaheuristic Strategies in Constraint Programming.- General Purpose Metrics for Solution Variety.- Controlled Pool Maintenance in Combinatorial Optimization.- Adaptive Memory Projection Methods for Integer Programming.
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Tabu Search (TS) and, more recently, Scatter Search (SS) have proved highly effective in solving a wide range of optimization problems, and have had a variety of applications in industry, science, and government. The goal of "Metaheuristic Optimization via Memory and Evolution: Tabu Search and Scatter Search" is to report original research on algorithms and applications of tabu search, scatter search or both, as well as variations and extensions having "adaptive memory programming" as a primary focus. Individual chapters identify useful new implementations or new ways to integrate and apply the principles of TS and SS, or that prove new theoretical results, or describe the successful application of these methods to real world problems.
(source: Nielsen Book Data)

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.
(source: Nielsen Book Data)