Multi-Objective Optimization using Evolutionary Algorithms
John Wiley & Sons, 05.07.2001 - 536 Seiten
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.
The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
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Salient Issues of MultiObjective Evolutionary Algorithms
Applications of MultiObjective Evolutionary Algorithms
NonElitist MultiObjective Evolutionary Algorithms
Elitist MultiObjective Evolutionary Algorithms
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Seite 508 - Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: Empirical results.
Seite 489 - T 1996 Evolutionary Algorithms in Theory and Practice (New York: Oxford University Press...
Seite 494 - Fogel, LJ., Angeline, PJ and Fogel, DB (1995). An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines.
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