Learning Probabilistic Models for Enhanced Evolutionary Computation

In this chapter we survey a type of evolutionary algorithm (EA) in which the main operator of variation is based on the learning of and sampling from probabilistic models. The main contribution of these probabilistic model—building EAs resides in the fact that by learning a probabilistic model from selected solutions and subsequently drawing new solutions from the probability distribution represented by the probabilistic model, an inductive tool is provided for online identification of features of the problem’s structure. These features are then used to guide the search more efficiently towards the promising regions of the search space. Probabilistic model—building EAs are relatively new to the field of evolutionary computation. In this chapter we briefly motivate their use. Through different classes of probability distribution, different types of dependency can be expressed, resulting in different types of induction. We review the literature on related work by discussing different classes of probability distribution that have been used so far in probabilistic model—building EAs. We conclude this chapter by reflecting on the use and applicability of learning probabilistic models for enhanced evolutionary computation, as well as on future perspectives.

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