Matching inductive search bias and problem structure in continuous estimation of distribution algorithms

Research into the dynamics of Genetic Algorithms (GAs) has led to the ¯eld of Estimation{of{Distribution Algorithms (EDAs). For discrete search spaces, EDAs have been developed that have obtained very promising results on a wide variety of problems. In this paper we investigate the conditions under which the adaptation of this technique to continuous search spaces fails to perform optimization e±ciently. We show that without careful interpretation and adaptation of lessons learned from discrete EDAs, continuous EDAs will fail to perform e±cient optimization on even some of the simplest problems. We reconsider the most important lessons to be learned in the design of EDAs and subsequently show how we can use this knowledge to extend continuous EDAs that were obtained by straightforward adaptation from the discrete domain so as to obtain an improvement in performance. Experimental results are presented to illustrate this improvement and to additionally con¯rm experimentally that a proper adaptation of discrete EDAs to the continuous case indeed requires careful consideration. Key words: Estimation{of{distribution algorithms; Numerical optimization