By Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson
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Extra info for Advances in learning classifier systems: third international workshop, IWLCS 2000, Paris, France, September 15-16, 2000 : revised papers
OOF 1). #]. V. E. Goldberg, and W. Stolzmann 50% chance. Thus, ee of both classifiers will be set to true since there is no difference between the mark and the indicated state when applied successfully. 5)}]. This classifier now predicts correctly the deterministic changes due to the execution of N and further predicts that the last attribute will change to 1 with a 50% chance. Since this classifier always anticipates correctly, its quality q will increase over 90% and will consequently become part of the internal environmental representation.
Corne, M. Oates, E. Hart, P-L. Lanzi, E. Willem, Y. Li, B. C Fogarty (eds) Real-World Applications of Evolutionary Computing: Proceedings of the EvoNet Workshops - EvoRob 2000, Springer, pp339-346. B. (1992) Evolving Artificial Intelligence. PhD dissertation, University of California. W. J. (1991) Letter Recognition using Holland-style Adaptive Classifiers. Machine Learning 6:161-182. Goldberg, D. & Segrest, P. (1987) Finite Markov Chain Analysis of Genetic Algorithms. J. ) Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum, pp1-7.
1 (Figure 1). 4. 8. This can be seen in the graphs since the relative difference in the possible changes in population (j=i) is much less in the latter case; the probability of increasing the number of ones (j=i+1) is significantly lower when i’=N than when i’=0. However, in all cases P(i,i+1) > P(i,i-1). That is, regardless of whether the evaluating rule’s partner from the first match-set has action one or zero, its best action value is a one and the simple GA identifies this fact. The same general behaviour occurs under partner rule variance.