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

近年来，基于概率模型的进化算法越来越受到人们的关注。在该方案中，后代种群的产生根据估计的概率模型的父种群，而不是使用传统的重组和变异算子。该模型预计将反映问题的结构，因此，预计这种方法提供了更有效的混合能力比重组运营商在传统的遗传算法。这些算法被称为概率建模遗传算法（PMBGA）或分布估计算法（EDA）。在PMBGA中，更好的个体是从最初随机生成的群体中选择的，就像标准GA一样。然后，估计所选择的个体集合的概率分布，并根据该估计生成新的个体，形成下一代的候选解。在本研究中，我们研究一个分散式的PMBGA模型。结果表明，该模型在求解实参数优化问题时，具有比传统遗传算法更好的性能。本文还研究了置换域上的PMBGA算法，如TSP问题、调度问题、车辆路径问题等。所提出的方法，这是所谓的边缘直方图为基础的采样算法（EHBSA），也表现出更好的性能比传统的遗传算法在各种问题的置换域。

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.