High Classification Accuracy Does Not Imply Effective Genetic Search

Tim Kovacs¹ and Manfred Kerber²

¹University of Bristol, Bristol BS8 1UB, U.K.
kovacs@cs.bris.ac.uk
http://www.cs.bris.ac.uk/~kovacs

²University of Birmingham, Birmingham B15 2TT, U.K.
M.Kerber@cs.bham.ac.uk
http://www.cs.bham.ac.uk/~mmk

Abstract. Learning classifier systems, their parameterisation, and their rule discovery systems have often been evaluated by measuring classification accuracy on small Boolean functions. We demonstrate that by restricting the rule set to the initial random population high classification accuracy can still be achieved, and that relatively small functions require few rules. We argue this demonstrates that high classification accuracy on small functions is not evidence of effective rule discovery. However, we argue that small functions can nonetheless be used to evaluate rule discovery when a certain more powerful type of metric is used.

LNCS 3103, p. 785 ff.

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