Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Randomized algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Paper: The parallel genetic algorithm as function optimizer
Parallel Computing
Mutation matrix in evolutionary computation: an application to resource allocation problem
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
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
Spatial and temporal resource allocation for adaptive parallel genetic algorithm
UC'07 Proceedings of the 6th international conference on Unconventional Computation
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The concept of portfolio management of algorithm is implemented in a new architecture based on ideas of cooperating multi-agents. Each agent is a simple genetic algorithm with identical structure but possibly different parameters. We introduce a "resource allocation vector" to coordinate the computing resources allocated to each agent. We also encourage constructive collaboration among agents by the exchange of the individuals in the population of each genetic algorithm using an individual-migration matrix. The algorithm can be implemented in a serial computer and behaves statistically in a quasi-parallel manner. We have performed extensive statistical analysis using measures such as the mean and variance of the first passage time to solution. The existence of investment frontier in solving the Schaffer function problem is demonstrated and application to the solving of the traveling salesman problem is discussed. The results suggest a more effective way to utilize computing resources.