|Practical Approaches to Multi-Objective Optimization|
|Hybrid Representation for Compositional Optimization and Parallelizing MOEAs|
|Felix Streichert ; Holger Ulmer ; reas Zell|
|Others : http://drops.dagstuhl.de/opus/volltexte/2005/251/pdf/04461.StreichertFelix.Paper.251.pdf
PID : 6882
【 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.
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