DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces

TL;DR

DisCo-DSO introduces a joint discrete-continuous optimization method using generative models, significantly improving efficiency and performance in complex hybrid design problems compared to traditional decoupled approaches.

Contribution

It presents a novel joint optimization framework that learns a distribution over hybrid variables, outperforming existing methods in efficiency and effectiveness.

Findings

DisCo-DSO reduces objective function evaluations.

It is robust to non-differentiable objectives.

Demonstrates superior performance in complex tasks like decision tree reinforcement learning.

Abstract

We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression. We propose DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach that uses a generative model to learn a joint distribution over discrete and continuous design variables to sample new hybrid designs. In contrast to standard decoupled approaches, in which the discrete and continuous variables are optimized separately, our joint optimization approach uses fewer objective function evaluations, is robust against non-differentiable objectives, and learns from prior samples to guide the search, leading to significant improvement in performance and sample efficiency. Our experiments on a diverse set of optimization tasks demonstrate that the advantages of DisCo-DSO become increasingly evident as the complexity of the problem increases. In particular, we illustrate DisCo-DSO's superiority over the state-of-the-art methods for interpretable reinforcement learning with decision trees.