Symbolic Regression via Neural-Guided Genetic Programming Population Seeding

• URL: http://arxiv.org/abs/2111.00053v1
• Date: Fri, 29 Oct 2021 19:26:41 GMT
• Title: Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
• Authors: T. Nathan Mundhenk and Mikel Landajuela and Ruben Glatt and Claudio P. Santiago and Daniel M. Faissol and Brenden K. Petersen
• Abstract summary: Symbolic regression is a discrete optimization problem generally believed to be NP-hard. Prior approaches to solving the problem include neural-guided search and genetic programming. We propose a neural-guided component used to seed the starting population of a random restart genetic programming component.
• Score: 6.9501458586819505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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