By Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson
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Extra resources for Advances in learning classifier systems: third international workshop, IWLCS 2000, Paris, France, September 15-16, 2000 : revised papers
5 Maximal Generality and a Niche-Based GA Wilson’s "generalization hypothesis"  suggests that in the accuracy-based XCS, given two rules of equal accuracy with one more general than the other, the former will become dominant due to its increased chance of being involved in a reproduction event (since it is a member of more match sets). The model can be used to (simply) demonstrate the prerequisite of this hypothesis by considering the progress of the previous accurate rule 1:1 and the more general rule #:1 (class B) on the task described above.
E. Goldberg, and W. Stolzmann ACS with GA in Woods1 with Action Noise = 50% ACS with GA in Maze4 with Action Noise = 50% 100 80 60 40 No. size (/100) 100 80 60 40 No. size 20 0 0 20000 40000 60000 80000 100000 number of steps Fig. 8. Applying the ACS with PEEs and GA in Woods1 and Maze4 with additional noise results again in a decrease in size but also a slight decrease in the performance. PEEs. We have shown that the GA is able to substantially decrease the size of the population while the formation of the environmental representation stays very close to perfect.
In combination with the enhanced classifier structure, the learning mechanism results in a complete internal model of the environment rather than a condition-action-payoff model. e. cognitive processes). L. Lanzi, W. W. ): IWLCS 2000, LNAI 1996, pp. 37–51, 2001. V. E. Goldberg, and W. Stolzmann Stolzmann, Butz, Hoffmann, and Goldberg (2000) investigated the present cognitive capabilities, introduced two new ones and outlined possible future applications. Until now, the ACS considered all changing attributes as results from its actions.
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