A Framework for Distributed Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Genetic Algorithms in a Multi-Agent System
INTSYS '98 Proceedings of the IEEE International Joint Symposia on Intelligence and Systems
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Testing the Efficiency of JADE Agent Platform
ISPDC '04 Proceedings of the Third International Symposium on Parallel and Distributed Computing/Third International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Networks
G2DGA: an adaptive framework for internet-based distributed genetic algorithms
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Exploring selection mechanisms for an agent-based distributed evolutionary algorithm
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Genetic algorithm based multi-agent system applied to test generation
Computers & Education
Learning Cooperation in Collaborative Grid Environments to Improve Cover Load Balancing Delivery
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Advances in Evolutionary Computing for System Design
Advances in Evolutionary Computing for System Design
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Evolutionary/Genetic Programs (EPs) are powerful search techniques used to solve combinatorial optimization problems in many disciplines. Unfortunately, depending on the complexity of the problem, they can be very demanding in terms of computational resources. However, advances in Distributed Artificial Intelligence (DAI), Multi-Agent Systems (MAS) to be more specific, could help users to deal with this matter. In this paper we present an approach in which both technologies, EP and MAS, are combined together aiming to reduce the computational requirements, allowing a response within a reasonable period of time. This approach, called EP-MAS.Lib, is focusing on the interaction among agents in the MAS, and emphasizing on the optimization obtained by means of the evolutionary algorithm/technique. For evaluating the EP-MAS.Lib approach, the paper also presents a case study based on a problem related with the configuration of a neural network for a specific purpose.