Adding Knowledge And Efficient Data Structures To Evolutionary Programming: A Cultural Algorithm For Constrained Optimization

This paper proposes a technique in which domain knowledge obtained from the search is used to improve the performance of evolutionary programming. Our approach is based on the concept of a cultural algorithm and is applied to constrained optimization problems in which a map of the feasible region is used to guide the search more efficiently. Our implementation uses 2n-trees to store more efficiently this map of the feasible region. Our results indicate that the approach is able to produce very competitive results (we compared our results with respect to the homomorphous maps) at a considerably low computational cost.

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