A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Characteristics of scalability and their impact on performance
Proceedings of the 2nd international workshop on Software and performance
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Initial approaches to the application of islands-based parallel EDAs in continuous domains
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
The speciating island model: an alternative parallel evolutionary algorithm
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Adaptive algorithms for the dynamic distribution and parallel execution of agent-based models
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Grid computing for parallel bioinspired algorithms
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Adaptive genetic algorithm with mutation and crossover matrices
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Importance of information exchange in quasi-parallel genetic algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Adaptive genetic algorithm and quasi-parallel genetic algorithm: application to knapsack problem
LSSC'05 Proceedings of the 5th international conference on Large-Scale Scientific Computing
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
| Hi-index | 0.00 |
The topological features of the communication network between computing nodes in Parallel Genetic Algorithms, under the framework of the island model, is discussed in the context of both the local rate of information exchange between nodes, and the global exchange rate that measures the level of information flow in the entire network. For optimal performance of parallel genetic algorithm for a set of benchmark functions, the connectivity of the network can be found, corresponding to a global information exchange rate between 40-70%. This range is obtained by statistical analysis on the search for solutions of four benchmark problems: the 0-1 knapsack, the Weierstrass's function, the Ackley's function, and the Modified Shekel's foxholes function. Our method is based on the cutting of links of a fully connected network to gradually decrease the connectivity, and compare the performance of the genetic algorithm on each network. Suggestions for the protocol in applying this general guideline in the design of a good communication network for parallel genetic algorithms are made, where the islands are connected with 40% of links of a fully connected network before fine tuning the parameters of the island model to enhance performance in a specific problem.