An Adaptive Algorithm for Constrained Optimization Problems.

Adaptivity has become a key issue in Evolutionary Algorithms, since early works in Evolution Strategies. The idea of letting the algorithm adjust its own parameters for free is indeed appealing. This paper proposes to use adaptive mechanisms at the population level for constrained optimization problems in three important steps of the evolutionary algorithm: First, an adaptive penalty function takes care of the penalty coefficients according to the proportion of feasible individuals in the current population; Second, a Seduction/Selection strategy is used to mate feasible individuals with infeasible ones and thus explore the region around the boundary of the feasible domain; Last, selection is tuned to favor a given number of feasible individuals. A detailed discussion of the behavior of the algorithm on two small constrained problems enlights adaptivity at work. Finally, experimental results on eleven test cases from the literature demonstrate the power of this approach. [ABSTRACT FROM AUTHOR]