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Download Agent-Based Optimization by Ireneusz Czarnowski, Piotr Jędrzejowicz, Janusz Kacprzyk PDF

By Ireneusz Czarnowski, Piotr Jędrzejowicz, Janusz Kacprzyk

This quantity offers a suite of unique examine works via prime experts concentrating on novel and promising methods within which the multi-agent process paradigm is used to help, improve or change conventional ways to fixing tricky optimization difficulties. The editors have invited a number of famous experts to provide their options, instruments, and versions falling below the typical denominator of the agent-based optimization. The ebook contains 8 chapters protecting examples of software of the multi-agent paradigm and respective custom-made instruments to resolve tough optimization difficulties bobbing up in numerous components equivalent to desktop studying, scheduling, transportation and, extra often, disbursed and cooperative challenge fixing.

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Engineering Applications of Artificial Intelligence 23, 1158– 1172 (2010) 34. : Collaborative Machine Learning. , Risse, T. ) From Integrated Publication and Information Systems to Information and Knowledge Environments. LNCS, vol. 3379, pp. 173–182. Springer, Heidelberg (2005) 35. : Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics. , Pedreschi, D. ) ECML 2004. LNCS (LNAI), vol. 3201, pp. 168–179. Springer, Heidelberg (2004) 36. : Escaping Brittleness: The possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems.

3898, pp. 71–85. Springer, Heidelberg (2006) 59. : An Ant Colony Based System for Data Mining: Application to medical data. In: Proceedings of Genetic and Evolutionary Computation Conference, pp. 791–798 (2001) 60. : Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27(3), 313–331 (1997) 61. : A Generic Architecture for Adaptive Agents Based on Reinforcement Learning. Information Science 161, 37–55 (2004) 62. : Meta-learning in Distributed Data Mining Systems: Issues and Approaches.

5 shows parameters’ values, common to both algorithms used during experiments. 50 M. Boryczka and W. Bura The results obtained by the PAVN algorithm in comparison with the sequential version of the algorithm (NAVN) were: • The PAVN algorithm with the same number of cycles produced a little bit better results and the performance time was shorter (about 50%) for PAVN. • Increasing the number of cycles led to further improvement of the results (with the performance time similar to the sequential version).

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