How an optimal observer can smooth a landscape

Most metaheuristics try to find a good balance between exploitation and exploration to achieve their goals. The exploration efficiency is highly dependent on the size and ruggedness of the search space. A metaheuristic such as the simple genetic algorithm (SGA) is not totally suited to traverse very large landscapes, especially deceptive ones. The approach proposed here improves the exploration of the SGA by adding a second search process through the way solutions are coded. We will designate by "observer" each possible coding that aims at reducing the search space. Information on the quality and adequacy of one possible observer is obtained by adopting this specific coding and testing how SGA benefits from this search space reduction. The new approach investigated here trains the observers for a specific time and then decides which of them is the most suitable to solve the whole problem. Concretely, a second evolutionary stage has been added to evolve observers for the SGA. These observers aim at reducing the size and smoothing the ruggedness of the search space through a simplification of the genotype. To test the proposed approach, we apply it to the classical hierarchical if-and-only-if (HIFF) problem and measure the efficiency of our approach in term of quality and time.