Kozax: Flexible and Scalable Genetic Programming in JAX

TL;DR

Kozax is a flexible, scalable genetic programming framework built on JAX that efficiently evolves symbolic expressions for diverse scientific computing tasks, leveraging high-performance GPU computation.

Contribution

Kozax introduces a novel, scalable genetic programming framework in JAX with features like constant optimization and multi-tree evolution, enabling broad scientific applications.

Findings

Successfully discovers natural law equations

Recovers hidden dynamic variables

Evolves control policies and optimizes objectives

Abstract

Genetic programming is an optimization algorithm inspired by evolution which automatically evolves the structure of interpretable computer programs. The fitness evaluation in genetic programming suffers from high computational requirements, limiting the performance on difficult problems. Consequently, there is no efficient genetic programming framework that is usable for a wide range of tasks. To this end, we developed Kozax, a genetic programming framework that evolves symbolic expressions for arbitrary problems. We implemented Kozax using JAX, a framework for high-performance and scalable machine learning, which allows the fitness evaluation to scale efficiently to large populations or datasets on GPU. Furthermore, Kozax offers constant optimization, custom operator definition and simultaneous evolution of multiple trees. We demonstrate successful applications of Kozax to discover equations of natural laws, recover equations of hidden dynamic variables, evolve a control policy and optimize an objective function. Overall, Kozax provides a general, fast, and scalable library to optimize white-box solutions in the realm of scientific computing.