Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Exploiting process lifetime distributions for dynamic load balancing
ACM Transactions on Computer Systems (TOCS)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Biases in the Crossover Landscape
Proceedings of the 3rd International Conference on Genetic Algorithms
Sub optimal scheduling in a grid using genetic algorithms
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
RouteGA: A Grid Load Balancing Algorithm with Genetic Support
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
MAS Modeling Based on Organizations
Agent-Oriented Software Engineering IX
Grid load balancing using intelligent agents
Future Generation Computer Systems
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Agent-based Virtual Organizations are complex entities where dynamic collections of agents agree to share resources in order to accomplish a global goal. Virtual Organizations offer complex services that require of the cooperation of distributed agents. An important problem for the performance of the Virtual Organization is distributing the agents across the computational resources so that the system achieves a good load balancing. In this paper, a solution for the agent distribution across hosts in an agent-based Virtual Organization is proposed. The solution is based on a genetic algorithm that is meant to be applied just after the formation of the Virtual Organization. The developed genetic strategy uses an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. This proposal differs from current works since it takes into account load balancing, software requirements of the agents and trust issues. In order to validate the proposal, the designed genetic algorithm has been succesfully compared to different heuristic methods that solve the same addresed problem.