Editorial Introduction Special Issue on Estimation of Distribution Algorithms

Estimation of Distribution Algorithms (EDAs) are a set of Evolutionary Algorithms characterized by (i) the use of explicit probability models to recover the information of the selected individuals and to sample new solutions, and (ii) the possibility of naturally incorporating prior knowledge about the optimization problem to be solved. The EDA term was first coined by Mühlenbein and Paaß (1996); seminal papers about EDAs were written three years later (Etxeberria and Larrañaga (1999); Mühlenbein and Mahnig (1999); Pelikan et al. (1999)). Since then, there has been a growing interest in EDAs, which now constitute an established discipline in the field of Evolutionary Computation (EC). Evidence of its establishment is the great number of papers on EDAs published in the main EC conferences and in EC-related journals, as well as the tutorials given in the PPSN, CEC and GECCO conferences, and the edited book by Larran̈aga and Lozano (2002).