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Estimation of Distribution Algorithms (EDAs) are a new tool for Evolutionary Computation. Based on Genetic Algorithms (GAs) this new class of algorithms generalizes GAs by replacing the crossover and mutation operators by learning and sampling the probability distribution of the best individuals of the population at each iteration. In this paper we present an introduction to EDAs in the field of combinatorial optimization. The algorithms are organised taking the complexity of the probabilistic model used into account. We also provide some points to the literature.

Estimation of Distribution Algorithms Applied To Combinatorial Optimization Problems