Study on the Distributed Probabilistic Model-Building Genetic Algorithms for Real-Parameter Optimization

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Recently, there has been a growing interest in developing evolutionary algorithms based on probabilistic models. In this scheme, the offspring population is generated according to the estimated probabilistic model of the parent population instead of using traditional recombination and mutation operators. The model is expected to reflect the problem structure, and as a result it is expected that this approach provides more effective mixing capability than recombination operators in traditional GAs. These algorithms are called probabilistic model-building genetic algorithms(PMBGAs) or estimation of distribution algorithms(EDAs). In a PMBGA, better individuals are selected from an initially randomly generated population like in standard GAs. Then, the probability distribution of the selected set of individuals is estimated and new individuals are generated according to this estimate, forming candidate solutions for the next generation. The process is repeated until the termination conditions are satisfied.In this research, a distributed PMBGA model was studied. The results showed that the proposed model had much better performance than traditional GAs in solving real-parameter optimization problems. In this research, an approach of PMBGAs in permutation domains, such as TSP, scheduling problems, vehicle routing problems, was studied, as well. The proposed approach, which is called edge histogram based sampling algorithm(EHBSA), also showed much better performance than traditional GAs in various problems of permutation domains.