Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding

Terrell N. Mundhenk, Mikel Landajuela, Ruben Glatt, Claudio P. Santiago, Daniel faissol, Brenden K. Petersen

Keywords: symbolic regression, genetic programming, neural-guided search, reinforcement learning

Abstract: Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process. It is a discrete optimization problem generally believed to be NP-hard. Prior approaches to solving the problem include neural-guided search (e.g. using reinforcement learning) and genetic programming. In this work, we introduce a hybrid neural-guided/genetic programming approach to symbolic regression and other combinatorial optimization problems. We propose a neural-guided component used to seed the starting population of a random restart genetic programming component, gradually learning better starting populations. On a number of common benchmark tasks to recover underlying expressions from a dataset, our method recovers 65% more expressions than a recently published top-performing model using the same experimental setup. We demonstrate that running many genetic programming generations without interdependence on the neural-guided component performs better for symbolic regression than alternative formulations where the two are more strongly coupled. Finally, we introduce a new set of 22 symbolic regression benchmark problems with increased difficulty over existing benchmarks. Source code is provided at www.github.com/brendenpetersen/deep-symbolic-optimization.

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TL;DR: We use a hybrid genetic programming and neural-guided search approach to solve symbolic regression.

Code: https://github.com/brendenpetersen/deep-symbolic-optimization