Symbolic regression by random search
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Purpose: To compare symbolic regression by genetic programming (SRGP) with symbolic regression by random search (SRRS), a novel method for symbolic regression described herein. Methods: We limit our problem space to N binary trees, m terminals and n functions, then use a dense enumeration of full binary trees to perform uniform random sampling from the set of all permitted equations. We compare a single basic configuration of symbolic regression by genetic programming with symbolic regression by random search using 1000 randomly generated problems. We perform a hyperparameter search with 50 randomly generated symbolic regression problems and 198 randomly generated hyperparameter configurations, examining the performance of SRGP against SRRS. Results: For the single configuration experiment, SRGP outperformed SRRS in 49.0 tie in 24.8 was best in 65.6 were not tied in the hyperparameter search, SRGP was best in 44 48 does SRRS.
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