ISSN : 1796-217X
Volume : 4    Issue : 4    Date : June 2009

Improved Adaptive and Multi-group Parallel Genetic Algorithm Based on Good-point Set
Ruijiang Wang, Yihong Ru, and Qi Long
Page(s): 348-356
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This paper puts forward an adaptive genetic algorithm to solve the multi-group homogenization in
the solution space. The use of good-point set approach improves the initial population, ensuring
them a uniform distribution in the solution space. In the evolution, each population implements
independent genetic operations (selection, good-point set crossover, and mutation). The
introduction of adaptive operator makes crossover and mutation operator self-adaptive. As the
algorithm adopts a strategy of retaining the best, a space compression strategy can be designed
based on information entropy theory through the information of all sub-populations in the evolution
process, which ensures the algorithmic stability and fast convergence to the global optimal
solution. Furthermore, in order to explore the feasibility and effectiveness of the improved
multi-group parallel algorithm, optimization tests are implemented on some of the typical
multi-peak functions, and the results are compared with the analytic solution and optimal solution of
basic GA. The outcome suggests that the global searching ability and convergence of the improved
algorithm is far better than the basic one.

Index Terms
Good-point Set; Genetic Algorithm; Adaptive Operator; Information Entropy