Abstract:
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Covariance Matrix Adaptation - Evolution Strategies (CMA-ES) is a renowned state-of-the-art
black-box optimzation algorithm in the field of Evolutionary Computation. As real-world
optimzation problems began to be characterized as multimodal, CMA-ES was subject to
modifications to improve it’s performance on these problems. The rise of multimodal problems
presented a new challenge for optimzation algorithms which is avoiding local optima while
trying to find the global optimum.
The CMA-ES algorithm, although powerful, is not guaranteed to beat this challenge as it may be
sampling in an area which contains basins of attraction where the global optimum does not
reside. This could be attributed to the fact that CMA-ES uses a single-model Estimation
Distribution Algorithm (EDA) to determine a single point in the problem landscape from which
to perform sampling.
This research investigates the performance of CMA-ES on several multimodal and unimodal
problems using two different EDAs with an overlapping model to perform sampling within the
problem landscape. This proposed system, Dualcenter-CMA-ES (DC-CMA-ES) outperforms
IPOP-CMA-ES on complex multimodal functions, especially as problem dimensionality
increases. |