Numerical optimization with real-valued estimation-of-distribution algorithms

Abstract

In this chapter we focus on the design of real-valued estimation-of-distribution algorithms (EDAs) for the task of numerical optimization. Here, both the problem variables as well as their encoding are real values. Concordantly, the type of probability distribution to be used for estimation and sampling in the EDA is continuous. In this chapter we indicate the main challenges in this area. Furthermore, we review the existing literature to indicate the current EDA practice for realvalued numerical optimization. Based on observations originating from this existing research and on existing work in the literature regarding dynamics of continuous EDAs, we draw some conclusions about the feasibility of existing EDA approaches. Also we provide an explanation for some observed deficiencies of continuous EDAs as well as possible improvements and future directions of research in this branch of EDAs. © Springer-Verlag Berlin Heidelberg 2006.