Punctuated equilibria: a parallel genetic algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Why DGAs work well on GA-hard functions?
New Generation Computing
Using Disruptive Selection to Maintain Diversity in GeneticAlgorithms
Applied Intelligence
How Genetic Algorithms Work: A Critical Look at Implicit Parallelism
Proceedings of the 3rd International Conference on Genetic Algorithms
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Fine-Grained Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A genetic algorithm with disruptive selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
According to the Neo-Darwinist, natural selection can be classified into three categories: directional selection, disruptive selection, and stabilizing selection. Traditional genetic algorithms can be viewed as a process of evolution based on directional selection that gives more chances of reproduction to superior individuals. However, this strategy sometimes is myopic and is apt to trap the search into a local optimal. Should we restrict genetic algorithms to direction selection? No! First, we show that stabilizing selection and disruptive selection are complementary and that hybridize them may supersede directional selection. Then, we adopt an island model of parallel genetic algorithms on which two types of selection strategies are applied to two subpopulations that both evolve independently and migration is allowed between them periodically. Experimental results show that the cooperation of disruptive selection and stabilizing selection is an effective and robust way in the genetic algorithms.