Memetic Algorithms' Home Page Memetic Algorithms is a population-based approach for heuristic search in optimization problems. They have shown that they are orders of magnitude faster than traditional Genetic Algorithms for some problem domains. Basically, they combine local search heuristics with crossover operators. For this reason, some researchers have viewed them as Hybrid Genetic Algorithms . However, combinations with constructive heuristics or exact methods may also belong to this class of metaheuristics. Since they are most suitable for MIMD parallel computers and distributed computing systems (including heterogeneous systems) as those composed by networks of workstations, they have also received the dubious denomination of Parallel Genetic Algorithms . Other researchers known it as Genetic Local Search The first use of the term Memetic Algorithms in the computing literature has appeared in 1989 in On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms . That paper discussed a heuristic we developed with Michael G. Norman which used Simulated Annealing for local search with a competitive and cooperative game between agents, interspersed with the use of a crossover operator (Caltech Concurrent Computation Program, Report. 790, 1989). Our method addressed the Traveling Salesman Problem as a representative test-bed. The method is gaining wide acceptance, in particular in well-known combinatorial optimization problems where large instances have been solved to optimality and where other metaheuristics have failed. An open research issue is to understand which features of the representation chosen had lead to characteristics of the objective functions which are efficiently exploited by a memetic approach. | |
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