By Martin V. Butz (auth.), Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson (eds.)
This publication constitutes the completely refereed post-proceedings of the 4th overseas Workshop on studying Classifier structures, IWLCS 2001, held in San Francisco, CA, united states, in July 2001.
The 12 revised complete papers provided including a unique paper on a proper description of ACS have undergone rounds of reviewing and development. the 1st a part of the publication is dedicated to theoretical problems with studying classifier structures together with the impact of exploration method, self-adaptive classifier platforms, and using classifier structures for social simulation. the second one half is dedicated to purposes in numerous fields reminiscent of info mining, inventory buying and selling, and tool distributionn networks.
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Additional info for Advances in Learning Classifier Systems: 4th International Workshop, IWLCS 2001 San Francisco, CA, USA, July 7–8, 2001 Revised Papers
Editors. 2. H. (1986). Escaping the brittleness: The possibilities of general purpose learning algorithms applied to parallel rule-based systems. ): Machine learning, an artiﬁcial intelligence approach, vol. II, chapter 20, pp. 593-623. Morgan Kaufmann. 42 ´ ee and Cathy Escazut Gilles En´ 3. E. (1989). Genetic algorithms in search, optimisation, and machine learning. Reading, MA: Addison-Wesley. 4. W. (1995). Classiﬁer ﬁtness based on accuracy. Evolutionary Computation, 3(2), pp. 149-175. 5.
The fifth column describes the growth of the set of compact classifiers. This column is a description of the compact classifiers whose count is given in the fourth column. It is an interesting question whether a classifier system, which is capable of compacting its solutions into highly compressed sets of classifiers with perfect performance on the IMP, will have increases in run time, steps to solution, and so forth that are near-linear (like the increase in size of the compact solution sets and in the signal size), or exponential in the size of the IMP, like the rate of increase in size of the number of signals, or the rate of increase in the size of the search space of possible classifiers.
They evolve toward a solution without any external help. When the problem is very intricate it is useful to have diﬀerent systems, each of them being in charge with an easier part of the problem. The set of all the entities responsible for the resolution of each sub-task, forms a multi-agent system. Agents have to learn how to exchange information in order to solve the main problem. In this paper, we deﬁne the minimal requirements needed by a multi-agent classiﬁer system to evolve communication.
Advances in Learning Classifier Systems: 4th International Workshop, IWLCS 2001 San Francisco, CA, USA, July 7–8, 2001 Revised Papers by Martin V. Butz (auth.), Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson (eds.)