Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parallel Genetic Algorithms Population Genetics and Combinatorial Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Fine-Grained Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
The Distributed Genetic Algorithm Revisited
Proceedings of the 6th International Conference on Genetic Algorithms
Significance of Locality and Selection Pressure in the Grand Deluge Evolutionary Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Global Selection Methods for Massively Parallel Computers
Selected Papers from AISB Workshop on Evolutionary Computing
Global Optimisation by Evolutionary Algorithms
PAS '97 Proceedings of the 2nd AIZU International Symposium on Parallel Algorithms / Architecture Synthesis
Visualization of a Parallel Genetic Algorithm in Real Time
AMT '01 Proceedings of the 6th International Computer Science Conference on Active Media Technology
| Hi-index | 0.00 |
The cellular genetic algorithm (CGA) combines GAs with cellular automata by spreading an evolving population across a pseudolandscape. In this study we use insights from ecology to introduce new features, such as disasters and connectivity changes, into the algorithm. We investigate the performance and behaviour of the algorithm on standard GA hard problems. The CGA has the advantage of avoiding premature convergence and outperforms standard GAs on particular problems. A potentially important feature of the algorithm's behaviour is that the fitness of solutions frequently improves in large jumps following disturbances (culling by patches).