Search Results for "problem a"

XBASIC development of ruleset based variation and selection of shapes in an evolutionary paradigm. This program demonstrates the exploration of open-ended problem spaces by random variation and rule-based selection.

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems. To address this problem, we present an efficient genetic algorithm by the combination with clustering methods. The high efficiency of the proposed method results from the fitness estimation and the schema discovery of partial individuals in current population and. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known benchmark problems in the term of optimization accuracy.