Hybrid Representation for Compositional Optimization and Parallelizing MOEAs

Practical Approaches to Multi-Objective Optimization

计算机科学;物理学

Felix Streichert ; Holger Ulmer ; reas Zell

Others  :  http://drops.dagstuhl.de/opus/volltexte/2005/251/pdf/04461.StreichertFelix.Paper.251.pdf
PID  :  6882

学科分类：计算机科学（综合）

来源: CEUR

【 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.