Evolutionary Multi-Objective Optimization is an expanding field of research. This book brings a collection of papers with some of the most recent advances in this field. The topic and content is currently very fashionable and has immense potential for practical applications and includes contributions from leading researchers in the field. Assembled in a compelling and well-organised fashion, Evolutionary Computation Based Multi-Criteria Optimization will prove beneficial for both academic and industrial scientists and engineers engaged in research and development and application of evolutionary algorithm based MCO. Packed with must-find information, this book is the first to comprehensively and clearly address the issue of evolutionary computation based MCO, and is an essential read for any researcher or practitioner of the technique.
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Inside This Book
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First Sentence:
Even though some real-world problems can be reduced to a matter of a single objective very often it is hard to define all the aspects in terms of a single objective. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
spectrum modal analysis, linear convergence order, continuous decision regions, dominating offspring, global best value, parameterized objective function, test problem design, gbest particle, elastic design response spectrum, ith objective function, mutation strength, multiobjective evolutionary algorithms, seismic loading conditions, multiobjective case, evolutionary multiobjective optimization, inheritance probabilities, pareto set, elite archive, scalarizing functions, multiple knapsack problem, archive size, vector evaluated genetic algorithms, multiobjective optimization problems, artificial accelerograms, optimization using evolutionary algorithms Key Phrases - Capitalized Phrases (CAPs): (learn more)
International Conference, Coello Coello, Lecture Notes, Evolutionary Multi-Criterion Optimization, Morgan Kaufmann Publishers, Service Center, Parallel Problem Solving, New York, San Francisco, Multi-Objective Genetic Algorithm, Multiobjective Genetic Algorithm, Van Veldhuizen, Distributed Cooperation, Kluwer Academic Publishers, New Jersey, Distributed Environment Scheme, San Mateo, Adaptive Computing, Genetic Chromodynamics, World Congress, Environmental Scheme, Particle Swarm Inspired Evolutionary Algorithm, Swiss Federal Institute of Technology, Technical Report, Air Force Institute of Technology New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Even though some real-world problems can be reduced to a matter of a single objective very often it is hard to define all the aspects in terms of a single objective. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
spectrum modal analysis, linear convergence order, continuous decision regions, dominating offspring, global best value, parameterized objective function, test problem design, gbest particle, elastic design response spectrum, ith objective function, mutation strength, multiobjective evolutionary algorithms, seismic loading conditions, multiobjective case, evolutionary multiobjective optimization, inheritance probabilities, pareto set, elite archive, scalarizing functions, multiple knapsack problem, archive size, vector evaluated genetic algorithms, multiobjective optimization problems, artificial accelerograms, optimization using evolutionary algorithms Key Phrases - Capitalized Phrases (CAPs): (learn more)
International Conference, Coello Coello, Lecture Notes, Evolutionary Multi-Criterion Optimization, Morgan Kaufmann Publishers, Service Center, Parallel Problem Solving, New York, San Francisco, Multi-Objective Genetic Algorithm, Multiobjective Genetic Algorithm, Van Veldhuizen, Distributed Cooperation, Kluwer Academic Publishers, New Jersey, Distributed Environment Scheme, San Mateo, Adaptive Computing, Genetic Chromodynamics, World Congress, Environmental Scheme, Particle Swarm Inspired Evolutionary Algorithm, Swiss Federal Institute of Technology, Technical Report, Air Force Institute of Technology New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 48
books:
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- Multi-Objective Optimization Using Evolutionary Algorithms by Kalyanmoy Deb on 4 pages
- New Jersey: With an Historical Guide (The States and the Nation series) by T. Fleming on page 26, page 28, and page 32
- Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic Algorithms and Evolutionary Computation) by Carlos A. Coello Coello on page 22, page 144, and page 251
- Evolutionary Computation by A.E. Eiben on page 30, and page 52
- Nonlinear Multiobjective Optimization (International Series in Operations Research & Management Science) by Kaisa Miettinen on page 22, and page 145
See all 48 books this book cites
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