Evolution algorithms in combinatorial optimization

Abstract Evolution algorithms for combinatorial optimization have been proposed in the 70's. They did not have a major influence. With the availability of parallel computers, these algorithms will become more important. In this paper we discuss the dynamics of three different classes of evolution algorithms: network algorithms derived from the replicator equation, Darwinian algorithms and genetic algorithms inheriting genetic information. We present a new genetic algorithm which relies on intelligent evolution of individuals. With this algorithm, we have computed the best solution of a famous travelling salesman problem. The algorithm is inherently parallel and shows a superlinear speedup in multiprocessor systems.

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