Structure and Performance of Fine-Grain Parallelism in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Serial and Parallel Genetic Algorithms as Function Optimizers
Proceedings of the 5th International Conference on Genetic Algorithms
An Analysis of the Effects of Neighborhood Size and Shape on Local Selection Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Comparing Synchronous and Asynchronous Cellular Genetic Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Examining the Effect of Elitism in Cellular Genetic Algorithms Using Two Neighborhood Structures
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Polynomial selection scheme with dynamic parameter estimation in cellular genetic algorithm
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Effect of topology on diversity of spatially-structured evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Dynamic Fault-Tolerant three-dimensional cellular genetic algorithms
Journal of Parallel and Distributed Computing
Cellular genetic algorithms without additional parameters
The Journal of Supercomputing
Self organizing classifiers and niched fitness
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Spatially structured evolutionary algorithms (EAs) have shown to be endowed with useful features for global optimization. Distributed EAs (dEA) and cellular EAs (cEA) are two of the most widely known types of structured algorithms. In this paper we deal with cellular EAs. Two important parameters guiding the search in a cEA are the population topology and the neighborhood defined on it. Here we first review some theoretical results which show that a cEA with a 2D grid can be easily tuned to shift from exploration to exploitation. We initially make a study on the relationship between the topology and the neighborhood by defining a ratio measure between they two. Then, we encompass a set of tests aimed at discovering the performance that different ratio values have on different classes of problems. We find out that, with the same neighborhood, rectangular grids have some advantages in multimodal and epistatic problems, while square ones are more efficient for solving deceptive problems and for simple function optimization. Finally, we propose and study a cEA in which the ratio is dynamically changed.