|
Abstract : |
Very recently many researchers, with backgrounds in parallel computing, started to develop hybrids of traditional genetic algorithms. The main departure from standard genetic algorithms is that these new methods incorporate specific heuristics for the problem at hand (drawing on a tradition which has roots outside the genetic framework) and which we apply within a stochastic game that exerts a selective pressure. The heuristics are used for periods of individual optimization, that is when agents do not interact. New computational results for the Traveling Salesman Problem will be presented in this paper. The approach is prepared to include Tabu Search techniques, introducing a new crossover operator (which is called Random Respectful Corner Recombination) and a special pair of a topology and set of rules for the interaction between agents. The approach has a natural parallelism and a feature called superlinear speed-up will also be discussed., |
