Design of parallel estimation of distribution algorithms

Abstract

This chapter focuses on the parallelization of Estimation of Distribution Algorithms (EDAs). More specifically, it presents guidelines for designing efficient parallel EDAs that employ parallel fitness evaluation and parallel model building. Scalability analysis techniques are employed to identify and parallelize the main performance bottlenecks to ensure that the achieved speedup grows almost linearly with the number of utilized processors. The proposed approach is demonstrated on the parallel Mixed Bayesian Optimization Algorithm (MBOA). We determine the time complexity of parallel MBOA and compare this complexity with experimental results obtained on a set of random instances of the spin glass optimization problem. The empirical results fit well the theoretical time complexity, so the scalability and efficiency of parallel MBOA for unknown spin glass instances can be predicted. © Springer-Verlag Berlin Heidelberg 2006.