The roles of local search, model building and optimal mixing in evolutionary algorithms from a bbo perspective

The inclusion of local search (LS) techniques in evolutionary algorithms (EAs) is known to be very important in order to obtain competitive results on combinatorial and real-world optimization problems. Often however, an important source of the added value of LS is an understanding of the problem that allows performing a partial evaluation to compute the change in quality after only small changes were made to a solution. This is not possible in a Black-Box Optimization (BBO) setting. Here we take a closer look at the added value of LS when combined with EAs in a BBO setting. Moreover, we consider the interplay with model building, a technique commonly used in Estimation-of-Distribution Algorithms (EDAs) in order to increase robustness by statistically detecting and exploiting regularities in the optimization problem. We find, using two standardized hard BBO problems from EA literature, that LS can play an important role, especially in the interplay with model building in the form of what has become known as substructural LS. However, we also find that optimal mixing (OM), which indicates that operations in a variation operator are directly checked whether they lead to an improvement, is a superior combination of LS and EA.

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