Adaptive representation for single objective optimization

A new technique called Adaptive Representation Evolutionary Algorithm (AREA) is proposed in this paper. AREA involves dynamic alphabets for encoding solutions. The proposed adaptive representation is more compact than binary representation. Genetic operators are usually more aggressive when higher alphabets are used. Therefore the proposed encoding ensures an efficient exploration of the search space. This technique may be used for single and multiobjective optimization. We treat the case of single objective optimization problems in this paper. Despite its simplicity the AREA method is able to generate a population converging towards optimal solutions. Numerical experiments indicate that the AREA technique performs better than other single objective evolutionary algorithms on the considered test functions.