A novel clustering fitness sharing genetic algorithm

Authors:
Xinjie Yu
Affiliations:
State Key Lab of Power Systems, Dept. of Electrical Engineering, Tsinghua University, Beijing, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

Citing 6
Cited 0

Reducing bias and inefficiency in the selection algorithm

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms with sharing for multimodal function optimization

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
An Investigation of Niche and Species Formation in Genetic Function Optimization

Proceedings of the 3rd International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection

Proceedings of the 6th International Conference on Genetic Algorithms
Fitness sharing and niching methods revisited

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The hybrid multimodal optimization algorithm that combines a novel clustering method and fitness sharing method is presented in this paper. The only parameter required by the novel clustering method is the peak number. The clustering criteria include minimizing the square sum of the inner-group distance, maximizing the square sum of the inter-group distance, and the fitness value of the individuals. After each individual has been classified to the certain cluster, fitness sharing genetic algorithm is used to find multiple peaks simultaneously. The empirical study of the benchmark problems shows that the proposed method has satisfactory performance.