Abstract
This paper details the implementation of a strongly-typed evolutionary programming system (STEPS) and its application to concept learning from highly-structured examples. STEPS evolves concept descriptions in the form of program trees. Predictive accuracy is used as the fitness function to be optimised through genetic operations. Empirical results with representative applications demonstrate promise.
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© 1999 Springer-Verlag Wien
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Kennedy, C.J., Giraud-Carrier, C. (1999). An Evolutionary Approach to Concept Learning with Structured Data. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_56
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_56
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
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