Distributed faulty sensor detection
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Probability collectives multi-agent systems: a study of robustness in search
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Multiple objective optimisation applied to route planning
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A study of probability collectives multi-agent systems on optimization and robustness
Transactions on computational collective intelligence IV
Combinatorial optimization in Biology using Probability Collectives Multi-agent Systems
Expert Systems with Applications: An International Journal
Multi-objective probability collectives
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
A probability collectives approach with a feasibility-based rule for constrained optimization
Applied Computational Intelligence and Soft Computing
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We compare Genetic Algorithms (GA's) with Probability Collectives (PC), a new framework for distributed optimization and control. In contrast to GA's, PC-based methods do not update populations of solutions. Instead they update an explicitly parameterized probability distribution p over the space of solutions. That updating of p arises as the optimization of a functional of p. The functional is chosen so that any p that optimizes it should be p peaked about good solutions. The PC approach has deep connections with both game theory and statistical physics. We review the PC approach using its motivation as the information theoretic formulation of bounded rationality for multi-agent systems (MAS). It is then compared with GA's on a diverse set of problems. To handle high dimensional surfaces, in the PC method investigated here p is restricted to a product distribution. Each distribution in that product is controlled by a separate agent. The test functions were selected for their difficulty using either traditional gradient descent or genetic algorithms. On those functions the PC-based approach significantly outperforms traditional GA's in both rate of descent, trapping in false minima, and long term optimization.