This dissertation presents principles, techniques, and performance of evolutionary computation optimization methods. Evolutionary computation concepts examined are algorithm convergence, population diversity and sizing, genotype and phenotype partitioning, archiving, BB concepts, parallel evolutionary algorithm (EA) models, robustness, visualization of evolutionary process, and performance in terms of e ectiveness and e ciency. Additional contributions include the extension of explicit BB de nitions to clarify the meanings for good single and multiobjective BBs and a new visualization technique is developed for viewing genotype, phenotype, and the evolutionary process in nding Pareto front vectors. The culmination of this research is explicit BB state-of-the-art MOEA technology based on the MOEA design, BB classi er type assessment, solution evolution visualization, and insight into MOEA test metric validation and usage as applied to the following: test suite, deception, bioinformatics, unmanned vehicle -ight pattern, and digital symbol set design MOPs.
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