Online Discovery of Search Objectives for Test-based Problems

In [13], we proposed disco, a method that automatically identifies the groups of tests for which the candidate solutions behave similarly. Each such group gives rise to a derived objective, which together guide the search algorithm in multi-objective fashion. When applied to several well-known test-based problems, the proposed approach significantly outperforms the conventional two-population coevolution. This opens the door to efficient and generic countermeasures to premature convergence for both coevolutionary and evolutionary algorithms applied to problems featuring aggregating fitness functions.