Playing in the Objective Space: Coupled Approximators for Multi-Objective Optimization

This paper presents a method of integrating computational intelligence with the operators used in evolutionary algorithms. We investigate approximation models of the objective function and its inverse and propose two simple algorithms that use these coupled approximators to optimize multi-objective functions. This method is a break from traditional approach used by standard cross-over and mutation operators, which only explore the objective space through "near-blind" manipulation of solutions in the parameter space. Fundamentally, our proposed intelligent operators use learned models of the coupling between the objective space and the parameter space to generate successively better solutions by extrapolating (or interpolating) from known solutions directly in the objective space. We term our implementation of the developed techniques as the coupled approximators evolutionary algorithm (CAEA). Promising empirical results with the DTLZ test suite prompt us to suggest several avenues for future research including combination with local search methods, incorporation of domain-knowledge and more efficient search algorithms.