This chapter demonstrates a novel method combining particle swarm, differential evolution, and genetic programming to build a symbolic regression tool for large-scale, time-constrained regression-classification problems. In a previous paper we experimented with large scale symbolic regression. Here we describe in detail the enhancements and techniques employed to support largescale, time—constrained regression and classification. 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. We discuss in some detail the construction of the fitness function, the use of abstract grammars to combine genetic programming with differential evolution and particle swarm agents, and the use of context-aware crossover.
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Korns, M.F. (2008). Large-Scale, Time-Constrained Symbolic Regression-Classification. In: Riolo, R., Soule, T., Worzel, B. (eds) Genetic Programming Theory and Practice V. Genetic and Evolutionary Computation Series. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-76308-8_4
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