Titlebook: Evolutionary Algorithms for Solving Multi-Objective Problems; Carlos A. Coello Coello,Gary B. Lamont,David A. Va Textbook 2007Latest editi

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Alternative Metaheuristics,her search techniques (e.g., Tabu search and simulated annealing) have proved to have very good performance in many combinatorial (as well as other types of) optimization problems, it is only natural to think of extensions of such approaches to deal with multiple objectives..The Operations Research

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Piero P. Foà,T. Adesanya Ige Grillo6.1 lists contemporary efforts reflecting MOEA theory development. In essence, a MOEA is searching for optimal elements in a partially ordered set or in the Pareto optimal set. Thus, the concept of convergence to . and . is integral to the MOEA search process.

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MOEA Theory and Issues,6.1 lists contemporary efforts reflecting MOEA theory development. In essence, a MOEA is searching for optimal elements in a partially ordered set or in the Pareto optimal set. Thus, the concept of convergence to . and . is integral to the MOEA search process.

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Heinz P. R. Seeliger,Herbert WernerMOEAs are adaptive stochastic search techniques classified under the umbrella of soft computing; generic EAs such as Genetic Algorithms, Evolution Strategies, Evolutionary Programming, and Genetic Programming are all successfully used in MOEA implementations

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https://doi.org/10.1007/978-3-642-46187-3mic processes for Coevolutionary MOEAs (CMOEA) with each researcher’s efforts summarized, categorized, and analyzed. Some potential concept and future applications of MOEA coevolution are also suggested. Exercises, discussion questions, and possible research directions for MOEA local search and coevolution are presented at the end of the chapter.