A Comparative Study of EAG and PBIL on Large-Scale Global Optimization Problems

Abstract

Estimation of Distribution Algorithms (EDAs) use global statistical information effectively to sample offspring disregarding the location information of the locally optimal solutions found so far. Evolutionary Algorithm with Guided Mutation (EAG) combines global statistical information and location information to sample offspring, aiming that this hybridization improves the search and optimization process. This paper discusses a comparative study of Population-Based Incremental Learning (PBIL), a representative of EDAs, and EAG on large-scale global optimization problems. We implemented PBIL and EAG to build an experimental setup upon which simulations were run. The performance of these algorithms was analyzed in terms of solution quality and computational cost. We found that EAG performed better than PBIL in attaining a good quality solution, but the latter performed better in terms of computational cost. We also compared the performance of EAG and PBIL with MA-SW-Chains, the winner of CEC’2010, and found that the overall performance of EAG is comparable to MA-SW-Chains.