Decomposable Problems, Niching, and Scalability of Multiobjective Estimation of Distribution Algorithms

Details Decomposable Problems, Niching, and Scalability of Multiobjective Estimation of Distribution Algorithms Decomposable Problems, Niching, and Scalability of Multiobjective Estimation of Distribution Algorithms

2005-02-12

arxiv.org/abs/cs/0502057v1

Computer Science

Neural and Evolutionary Computing

Submitted to Genetic and Evolutionary Computation Conference, GECCO-2005

Scientific paper

The paper analyzes the scalability of multiobjective estimation of distribution algorithms (MOEDAs) on a class of boundedly-difficult additively-separable multiobjective optimization problems. The paper illustrates that even if the linkage is correctly identified, massive multimodality of the search problems can easily overwhelm the nicher and lead to exponential scale-up. Facetwise models are subsequently used to propose a growth rate of the number of differing substructures between the two objectives to avoid the niching method from being overwhelmed and lead to polynomial scalability of MOEDAs.

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