Abstract
This chapter gives a narrative of the problems we encountered using genetic programming to build a symbolic regression tool for large-scale, time-constrained regression problems. It describes in detail the problems encountered, the commonly held beliefs challenged, and the techniques required to achieve reasonable performance with large-scale, time-constrained regression. We discuss in some detail the selection of the compilation tools, the construction of the fitness function, the chosen system grammar (including internal functions and operators), and the chosen system architecture (including multiple island populations). Furthermore in order to achieve the level of performance reported here, of necessity, we borrowed a number of ideas from disparate schools of genetic programming and recombined them in ways not normally seen in the published literature.
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Korns, M.F. (2007). Large-Scale, Time-Constrained Symbolic Regression. In: Riolo, R., Soule, T., Worzel, B. (eds) Genetic Programming Theory and Practice IV. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-49650-4_18
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DOI: https://doi.org/10.1007/978-0-387-49650-4_18
Publisher Name: Springer, Boston, MA
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