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2004

Multimodal Function Optimization Using Local Ruggedness Information

8 years 6 months ago
Multimodal Function Optimization Using Local Ruggedness Information
In multimodal function optimization, niching techniques create diversification within the population, thus encouraging heterogeneous convergence. The key to the effective diversification is to identify the similarity among individuals. Without knowledge of the fitness landscape, it is usually determined by uninformative assumptions. In this article, we propose a method to estimate the sharing distance for niching and the population size. Using the Probably Approximately Correct (PAC) learning theory and the -cover concept, we prove a PAC neighborhood of a local optimum exists for a given population size. The PAC neighbor distance is further derived. Within this neighborbood, we uniformly sample the fitness landscape and compute its subspace fitness distance correlation (FDC) coefficients. An algorithm for estimating the granularity feature is described. The sharing distance and the population size are determined when above procedure converges. Experiments demonstrate that by using the...
Jian Zhang 0007, Xiaohui Yuan, Bill P. Buckles
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where FLAIRS
Authors Jian Zhang 0007, Xiaohui Yuan, Bill P. Buckles
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