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Content Provider | ACM Digital Library |
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Author | Wolpert, David H. Huang, Chien-Feng Bieniawski, Stefan Strauss, Charlie E. M. |
Abstract | 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. |
Starting Page | 751 |
Ending Page | 752 |
Page Count | 2 |
File Format | |
ISBN | 1595930108 |
DOI | 10.1145/1068009.1068136 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2005-06-25 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Product distribution theory Estimation of distribution algorithms Probability collectives Genetic algorithms Multi-agent systems |
Content Type | Text |
Resource Type | Article |
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