The adoption of probabilistic models for the best individuals found so far is a powerful approach for evolutionary computation. Increasingly more complex models have been used by estimation of distribution algorithms (EDAs), which often result better effectiveness on finding the global optima for hard optimization problems. Supervised and unsupervised learning of Bayesian networks are very effective options, since those models are able to capture interactions of high order among the variables of a problem. Diversity preservation, through niching techniques, has also shown to be very important to allow the identification of the problem structure as much as for keeping several global optima. Recently, clustering was evaluated as an effective niching technique for EDAs, but the performance of simpler low-order EDAs was not shown to be much improved by clustering, except for some simple multimodal problems. This work proposes and evaluates a combination operator guided by a measure from information theory which allows a clustered low-order EDA to effectively solve a comprehensive range of benchmark optimization problems.
@cite_18: This paper presents a basic framework that facilitates the development of new multiple-deme parallel estimation of distribution algorithms (PEDAs). The aim is to carry over the migration effect that arises in multiple-deme parallel genetic algorithms (PGAs) into probability distribution of EDAs. The idea is to employ two kinds of probability vector (PV): one each for resident and immigrant candidates. The distribution of crossbred individuals (that virtually exist on both kinds of PV) is then utilized by a new type of crossover, the PV-wise crossover. A multiple-deme parallel population-based incremental learning (P2BIL) scheme is proposed as an application. The P2BIL scheme closely follows the proposed framework that includes a new learning strategy (i.e., PV update rule). Experimental results show that P2BIL generally exhibits solutions that compare favourably with those computed by an existing PGA with multiple demes, thereby supporting the validity of the proposed framework for designing multiple-deme PEDAs.
A similar approach to clustering is the parallelization of EDAs by adopting multiple subpopulations and migration. In @cite_18 , a simple recombination operator called PV-wise uniform crossover is proposed, which is similar to GA uniform crossover. After two parent PVs were selected for combination a new temporary PV is built by randomly selecting, for each gene, from which of the two parents should the binomial proportion be taken from. A new individual is sampled from this PV.