Component weighting functions for adaptive search with EDAs

This paper introduces the component weighting approach as a general optimization heuristic to increase the likelihood of escaping from local optima by dynamically modifying the fitness function. The approach is tested on the optimization of the simplified hydrophobic-polar (HP) protein problem using estimation of distribution algorithms (EDAs). We show that the use of component weighting together with statistical information extracted from the set of selected solutions considerably improve the results of EDAs for the HP problem. The paper also elaborates on the use of probabilistic modeling for the definition of dynamic fitness functions and on the use of combinations of models.