Krzysztof Krawiec


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We introduce potential fitness, a variant of fitness function that operates in the space of schemata and is applicable to tree-based genetic programing. The proposed evaluation al- gorithm estimates the maximum possible gain in fitness of an individual's direct offspring. The value of the potential fitness is calculated by analyzing the context semantics and subtree semantics for all contexts (schemata) of the evalu- ated tree. The key feature of the proposed approach is that a tree is rewarded for the correctly classiffed fitness cases, but it is not penalized for the incorrectly classified ones, provided that such errors are recoverable by substitution of an appropriate subtree (which is however not explicitly con- sidered by the algorithm). The experimental evaluation on a set of seven boolean benchmarks shows that the use of potential fitness may lead to better convergence and higher success rate of the evolutionary run.

@INPROCEEDINGS { krawiec08potential,
    AUTHOR = { Krzysztof Krawiec and Przemys┼éaw Polewski },
    TITLE = { Potential fitness for genetic programming },
    BOOKTITLE = { GECCO '08: Proceedings of the 2008 GECCO conference companion on Genetic and evolutionary computation },
    YEAR = { 2008 },
    PAGES = { 2175--2180 },
    ADDRESS = { New York, NY, USA },
    PUBLISHER = { ACM },
    ABSTRACT = { We introduce potential fitness, a variant of fitness function that operates in the space of schemata and is applicable to tree-based genetic programing. The proposed evaluation al- gorithm estimates the maximum possible gain in fitness of an individual's direct offspring. The value of the potential fitness is calculated by analyzing the context semantics and subtree semantics for all contexts (schemata) of the evalu- ated tree. The key feature of the proposed approach is that a tree is rewarded for the correctly classiffed fitness cases, but it is not penalized for the incorrectly classified ones, provided that such errors are recoverable by substitution of an appropriate subtree (which is however not explicitly con- sidered by the algorithm). The experimental evaluation on a set of seven boolean benchmarks shows that the use of potential fitness may lead to better convergence and higher success rate of the evolutionary run. },
    COMMENT = { ProjectELP },
    DOI = { http://doi.acm.org/10.1145/1388969.1389043 },
    ISBN = { 978-1-60558-131-6 },
    LOCATION = { Atlanta, GA, USA },
}


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