Multiobjective optimization using adaptive Pareto archived evolution strategy

摘要

This paper proposes a novel adaptive representation for evolutionary multiobjective optimization for solving a stock modeling problem. The standard Pareto achieved evolution strategy (PAES) uses real or binary representation for encoding solutions. Adaptive Pareto archived evolution strategy (APAES) uses dynamic alphabets for encoding solutions. APAES is applied for modeling two popular stock indices involving 4 objective functions. Further, two bench mark test functions for multiobjective optimization are also used to illustrate the performance of the algorithm. Empirical results demonstrate APAES performs well when compared to the standard PAES,.