By Alfonsas Misevicius (auth.), Dr Frans Coenen, Dr Alun Preece, Dr Ann Macintosh BSc, CEng (eds.)
Frans Coenen college of Liverpool, united kingdom This quantity contains the refereed technical papers provided at AI2003, the Twenty 3rd SGAI overseas convention at the idea, perform and alertness of man-made Intelligence, held in Cambridge in December 2003. The convention was once organised by way of SGAI, the British machine Society expert staff on man made Intelligence (previously often called SGES). The papers during this quantity current new and cutting edge advancements within the box, divided into sections on desktop studying, wisdom illustration and Reasoning, wisdom Acquisition, Constraint delight, Scheduling and average Language Processing. This year's prize for the easiest refereed technical paper used to be gained via a paper entitled a more robust Hybrid Genetic set of rules: New effects for the Quadratic project challenge by means of A. Misevicius (Department of functional Informatics, Kaunas college of expertise, Lithuania). SGAI gratefully recognizes the long term sponsorship of Hewlett-Packard Laboratories (Bristol) for this prize, which works again to the Eighties. this is often the 20th quantity within the learn and improvement sequence. the appliance circulation papers are released as a spouse quantity less than the identify functions and suggestions in clever platforms XI. On behalf of the convention setting up committee I should still wish to thank all those that contributed to the enterprise of this year's technical programme, specifically the programme committee contributors, the referees and our administrator Fiona Hartree and Linsay Turbert.
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Extra info for Research and Development in Intelligent Systems XX: Proceedings of AI2003, the Twenty-third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence
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Fogarty (1989). Varying the Probability of Mutation in the Genetic Algorithm. In J. D. ), Proc. of the Srd Int. Conf. on Genetic Algorithms, 104-109. San Mateo, CA: Morgan Kaufmann Publishers . [11] J . J. Grefenstette (1986). Optimization of Control Parameters for Genetic Algorithms. IEEE 1hlns. on Systems, Man and Cybernetics, 16(1): 122128. [12] D. E. Goldberg (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley. [13] D. E. Goldberg, B. Korb and K.
Here, 'temperature' is an abstract control parameter for the algorithm rather than a real physical measure. The algorithm starts with a high temperature, which is subsequently reduced slowly, usually in steps. e. e. in a balanced state. If this time is too short, the algorithm is likely to converge to a local minimum. The combination of temperature steps and cooling times is known as the annealing schedule, which is usually selected empirically. Figure I shows a typical run of a SA algorithm, optimising a real-world system [4].
R2 consists of four levels of schemas: level 0 (bottom level) is the same as R i, level 1 has 4 order-16 schemas of which each combines two adjacent schemas in level 0, level 2 contains 2 order-32 schemas each combining two adjacent schemas in levell, and finally level 3 (the optimal schema) combines the 2 schemas in level 2. The fitness of a bit string x for R i (x) and R2(x) is computed by summing the coefficients Ci corresponding to each of the given schema Si of which x is an instance. That is, Ri(x) and R2(x) are defined as follows: i=8 Ri(x) i=14 = L: Cidi(X) and R2(X) = L: Cidi(X) i=i i=i where di(X) = {1,if x E SijO,otherwise}.