Abstract
Mind e volutionary a lgorithm (MEA) uses ‘similartaxis’ operation and ‘dissimilation’ operation to imitate the human mind evolution to processes optimization, overcoming the prematurity and improving searching efficiency. But it has several defects: the generation of the initial population is blind, random and redundant; the addition of naturally washed out temporary subpopulations is monotonous; existing searching modes easily to fall into local convergence. This paper proposed Chao s Mind Evolutionary Algorithm. Two chaotic sequences produced in different ways bring adequate diversity to the population. As a result, the searching area is widened. Chaotic Mind Evolutionary Algorithm is used in a ntenna array synthesis in this paper. Computer simulations show that Chaos Mind Evolutionary Algorithm can be applied in optimization problems of uniformly-spaced linear array and the optimization result is better than that obtained from Genetic Algorithm.
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