Abstract
This chapter examines the use of Abstract Expression Grammars to perform the entire Symbolic Regression process without the use of Genetic Programming per se. The techniques explored produce a symbolic regression engine which has absolutely no bloat, which allows total user control of the search space and output formulas, which is faster, and more accurate than the engines produced in our previous papers using Genetic Programming. The genome is an all vector structure with four chromosomes plus additional epigenetic and constraint vectors, allowing total user control of the search space and the final output formulas. A combination of specialized compiler techniques, genetic algorithms, particle swarm, aged layered populations, plus discrete and continuous differential evolution are used to produce an improved symbolic regression sytem. Nine base test cases, from the literature, are used to test the improvement in speed and accuracy. The improved results indicate that these techniques move us a big step closer toward future industrial strength symbolic regression systems.
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Korns, M.F. (2011). Abstract Expression Grammar Symbolic Regression. In: Riolo, R., McConaghy, T., Vladislavleva, E. (eds) Genetic Programming Theory and Practice VIII. Genetic and Evolutionary Computation, vol 8. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7747-2_7
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DOI: https://doi.org/10.1007/978-1-4419-7747-2_7
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