Abstract
This chapter examines techniques for improving symbolic regression systems in cases where the target expression contains conditionals. In three previous papers we experimentedwith combining high performance techniques fromthe literature to produce a large scale, industrial strength, symbolic regression-classification system. Performance metrics across multiple problems show deterioration in accuracy for problems where the target expression contains conditionals. The techniques described herein are shown to improve accuracy on such conditional problems. Nine base test cases, from the literature, are used to test the improvement in accuracy. A previously published regression system combining standard genetic programming with abstract expression grammars, particle swarm optimization, differential evolution, context aware crossover and age-layered populations is tested on the nine base test cases. The regression system is enhanced with these additional techniques: pessimal vertical slicing, splicing of uncorrelated champions via abstract conditional expressions, and abstract mutation and crossover. The enhanced symbolic regression system is applied to the nine base test cases and an improvement in accuracy is observed.
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Korns, M.F. (2010). Symbolic Regression of Conditional Target Expressions. In: Riolo, R., O'Reilly, UM., McConaghy, T. (eds) Genetic Programming Theory and Practice VII. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1626-6_13
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DOI: https://doi.org/10.1007/978-1-4419-1626-6_13
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