Evolution through reputation: noise-resistant selection in evolutionary multi-agent systems
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Little attention has been paid, in depth, 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. Our investigation concentrates on the effectiveness with which social selection, in the form of reputation, can replace direct fitness observation as the selection bias in an evolutionary multi-agent system. We do this in two stages: In the first, we implement a peer-to-peer, adaptive Genetic Algorithm (GA), in which agents act as individual GAs that, in turn, evolve dynamically themselves in real-time, using the traditional evolutionary operators of fitness-based selection, crossover and mutation. In the second stage, we replace the fitness-based selection operator with a reputation-based one, in which agents choose their mates based on the collective past experiences of themselves and their peers. Our investigation shows that this simple model of distributed reputation can be successful as the evolutionary drive in such a system, exhibiting practically identical performance and scalability to direct fitness observation. Further, we discuss the effect of noise (in the form of “defective” agents) in both models. We show that the reputation-based model is significantly better at identifying the defective agents, thus showing an increased level of resistance to noise.