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Brock TR # CS-01-01 Abstract

An Examination of Lamarckian Genetic Algorithms    [PDF]
Cameron Wellock and Brian J Ross, July 2001.

In keeping with the spirit of Lamarckian evolution, variations on a simple genetic algorithm are compared, in which each individual is optimized prior to evaluation. Four different optimization techniques in all are tested: random hillclimbing, social (memetic) exchange, and two techniques using artificial neural nets (ANNs). These techniques are tested on a set of three sample problems: an instance of a minimum-spanning tree problem, an instance of a travelling salesman problem, and a problem where ANNs are evolved to generate a random sequence of bits. The results suggest that in general, social exchange provides the best performance, consistently outperforming the non-optimized genetic algorithm; results for other optimization techniques are less compelling.