Performance of Evolutionary Algorithms on Random Decomposable Problems

Martin Pelikan, Kumara Sastry, Martin V. Butz, David E. Goldberg

Abstract: This paper describes a class of random additively decomposable problems (rADPs) with and without interactions between the subproblems. The paper then tests the hierarchical Bayesian optimization algorithm (hBOA) and other evolutionary algorithms on a large number of random instances of the proposed class of problems. The results show that hBOA can scalably solve rADPs and that it significantly outperforms all other methods included in the comparison. Furthermore, the results provide a number of interesting insights into both the difficulty of a broad class of decomposable problems as well as the sensitivity of various evolutionary algorithms to different sources of problem difficulty. rADPs can be used to test other optimization algorithms.