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A Comparative Study of Probability Collectives Based Multi-agent Systems and Genetic AlgorithmsWe 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 works in both continuous and discrete problems. It does not suffer from the resolution limitation of the finite bit length encoding of parameters into GA alleles. It also 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. 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.
Document ID
20060016354
Acquisition Source
Ames Research Center
Document Type
Preprint (Draft being sent to journal)
Authors
Huang, Chien-Feng
(Los Alamos National Lab. NM, United States)
Wolpert, David H.
(NASA Ames Research Center Moffett Field, CA, United States)
Bieniawski, Stefan
(Stanford Univ. Stanford, CA, United States)
Strauss, Charles E. M.
(Los Alamos National Lab. NM, United States)
Date Acquired
August 23, 2013
Publication Date
January 1, 2005
Subject Category
Statistics And Probability
Distribution Limits
Public
Copyright
Other

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