Using Disruptive Selection to Maintain Diversity in GeneticAlgorithms
Applied Intelligence
A Novel Genetic Algorithm and its Application in Fuzzy Variable Structure Control of Fuel Cell
Journal of Intelligent and Robotic Systems
A novel and accelerated genetic algorithm
WSEAS Transactions on Systems and Control
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A synergistic selection strategy in the genetic algorithms
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A method for local tuning of fuzzy membership functions
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Niche Genetic Algorithm Based on Sexual Reproduction and Multimodal Function Optimization Problem
International Journal of Advanced Pervasive and Ubiquitous Computing
| Hi-index | 0.00 |
Genetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. Applying the “survival-of-the-fittest” principle, traditional genetic algorithms allocate more trials to above-average schemata. However, increasing the sampling rate of schemata that are above average does not guarantee convergence to a global optimum; the global optimum could be a relatively isolated peak or located in schemata that have large variance in performance. In this paper we propose a novel selection method, disruptive selection. This method adopts a nonmonotonic fitness function that is quite different from traditional monotonic fitness functions. Unlike traditional genetic algorithms, this method favors both superior and inferior individuals. Experimental results show that GAs using the proposed method easily find the optimal solution of a function that is hard for traditional GAs to optimize. We also present convergence analysis to estimate the occurrence ratio of the optima of a deceptive function after a certain number of generations of a genetic algorithm. Experimental results show that GAs using disruptive selection in some occasions find the optima more quickly and reliably than GAs using directional selection. These results suggest that disruptive selection can be useful in solving problems that have large variance within schemata and problems that are GA-deceptive