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2008

A diversity maintaining population-based incremental learning algorithm

11 years 8 months ago
A diversity maintaining population-based incremental learning algorithm
In this paper we propose a new probability update rule and sampling procedure for population-based incremental learning. These proposed methods are based on the concept of opposition as a means for controlling the amount of diversity within a given sample population. We prove that under this scheme we are able to asymptotically guarantee a higher diversity, which allows for a greater exploration of the search space. The presented probabilistic algorithm is specifically for applications in the binary domain. The benchmark data used for the experiments are commonly used deceptive and attractor basin functions as well as 10 common Travelling Salesman problem instances. Our experimental results focus on the effect of parameters and problem size on the accuracy of the algorithm as well as on a comparison to traditional population-based incremental learning. We show that the new algorithm is able to effectively utilize the increased diversity of opposition which leads to significantly impro...
Mario Ventresca, Hamid R. Tizhoosh
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2008
Where ISCI
Authors Mario Ventresca, Hamid R. Tizhoosh
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