ImprovEvolve: Ask AlphaEvolve to Improve the Input Solution and Then Improvise

TL;DR

ImprovEvolve enhances LLM-guided evolutionary algorithms by evolving a program that iteratively improves solutions, achieving state-of-the-art results on complex optimization problems with minimal human editing.

Contribution

The paper introduces ImprovEvolve, a novel approach that evolves a program to improve solutions, reducing cognitive load and surpassing previous results in challenging optimization tasks.

Findings

Achieved new state-of-the-art results for hexagon packing problems.

Improved the lower bound for the second autocorrelation inequality.

Lightly human-edited variants further enhanced results.

Abstract

Recent advances in LLM-guided evolutionary computation, particularly AlphaEvolve, have demonstrated remarkable success in discovering novel mathematical constructions and solving challenging optimization problems. In this article, we present ImprovEvolve, a simple yet effective technique for enhancing LLM-based evolutionary approaches such as AlphaEvolve. Given an optimization problem, the standard approach is to evolve program code that, when executed, produces a solution close to the optimum. We propose an alternative program parameterization that maintains the ability to construct optimal solutions while reducing the cognitive load on the LLM. Specifically, we evolve a program (implementing, e.g., a Python class with a prescribed interface) that provides the following functionality: (1) propose a valid initial solution, (2) improve any given solution in terms of fitness, and (3) perturb a solution with a specified intensity. The optimum can then be approached by iteratively applying improve() and perturb() with a scheduled intensity. We evaluate ImprovEvolve on challenging problems from the AlphaEvolve paper: hexagon packing in a hexagon and the second autocorrelation inequality. For hexagon packing, the evolved program achieves new state-of-the-art results for 11, 12, 15, and 16 hexagons; a lightly human-edited variant further improves results for 14, 17, and 23 hexagons. For the second autocorrelation inequality, the human-edited program achieves a new state-of-the-art lower bound of 0.96258, improving upon AlphaEvolve's 0.96102.