Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Social trust: a cognitive approach
Trust and deception in virtual societies
Notions of reputation in multi-agents systems: a review
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
An evidential model of distributed reputation management
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
The Distributed Genetic Algorithm Revisited
Proceedings of the 6th International Conference on Genetic Algorithms
A Framework for Distributed Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Towards an Optimal Mutation Probability for Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
Supporting Trust in Virtual Communities
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Investigations in meta-GAs: panaceas or pipe dreams?
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
A Survey: Genetic Algorithms and the Fast Evolving World of Parallel Computing
HPCC '08 Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications
A lightweight coordination calculus for agent systems
DALT'04 Proceedings of the Second international conference on Declarative Agent Languages and Technologies
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Little attention has been paid to the relationship between fitness evaluation in evolutionary algorithms and reputation mechanisms in multi-agent systems, but if these could be related it opens the way for implementation of distributed evolutionary systems via multi-agent architectures. In this paper we investigate the effectiveness with which reputation can replace direct fitness observation as the selection bias in an evolutionary multi-agent system. We do this by implementing a peer-to-peer, self-adaptive genetic algorithm, in which agents act as individual GAs that, in turn, evolve dynamically themselves in real-time. The evolution of the agents is implemented in two alternative ways: First, using the traditional approach of direct fitness observation (self-reported by each agent), and second, using a simple reputation model based on the collective past experiences of the agents. Our research shows that this simple model of distributed reputation can be successful as the evolutionary drive in such a system. Further, we discuss the effect of noise (in the form of "defective" agents) in both models. We show that, unlike the fitness-based model, the reputation-based model manages to identify the defective agents successfully, thus showing a level of resistance to noise.