Hybrid Representation for Compositional Optimization and Parallelizing MOEAs

[摘要] In many real-world optimization problems sparse solution vectors are often preferred. Unfortunately, evolutionary algorithms can have problems to eliminate certain components completely especially in multi-modal or neutral search spaces. A simple extension of the realvalued representation enables evolutionary algorithms to solve these types of optimization problems more efficiently. In case of multi-objective opti- mization some of these compositional optimization problems show most peculiar structures of the Pareto front. Here, the Pareto front is often non-convex and consists of multiple local segments. This feature invites parallelization based on the ’divide and conquer’ principle, since subdi- vision into multiple local multi-objective optimization problems seems to be feasible. Therefore, we introduce a new parallelization scheme for multi-objectiveevolutionaryalgorithmsbasedonclustering.

[效力级别] 物理学 [学科分类] 计算机科学（综合）