A locally weighted metamodel for pre-selection in evolutionary optimization

The evolutionary algorithms are usually criticized for their slow convergence. To address this weakness, a variety of strategies have been proposed. Among them, the metamodel or surrogate based approaches are promising since they replace the original optimization objective by a metamodel. However, the metamodel building itself is expensive and therefore the metamodel based evolutionary algorithms are commonly applied to expensive optimization. In this paper, we propose an alternative metamodel, named locally weighted metamodel (LWM), for the pre-selection in evolutionary optimization. The basic idea is to estimate the objective values of candidate offspring solutions for an individual, and choose the most promising one as the offspring solution. Instead of building a global model as many other algorithms do, a LWM is built for each candidate offspring solution in our approach. The LWM based pre-selection is implemented in a multi-operator based evolutionary algorithm, and applied to a set of test instances with different characteristics. Experimental results show that the proposed approach is promising.