[Paper] Mathematical perspective on genetic algorithms with optimization guided operators

Source: arXiv - 2606.12279v1

Overview

Recent work in ML applies genetic algorithms at inference time to iteratively improve solutions to optimization problems. The basic mutation and recombination operators involved are qualitatively different from those studied classically. Mutations are no longer random; an ML algorithm mutates a solution with the goal of improving an objective. Similarly, recombination is not based on random collages of parent solutions. Instead, it is an ML optimization-based operator whose goal is to synthesize improved solutions from its inputs. Thus, these mutation and recombination operators are more likely to improve the objective, but their computational cost is much higher. We introduce a general model of genetic algorithms and formulating optimization in this model as a query-complexity problem, using the language of reinforcement learning. We then study specialized models. We show that some optimization problems require generation, mutation, and recombination to be solved. We then obtain qualitatively tight algorithms for a family of problems within this framework that captures the nontrivial role of diversity in the solution pool, a key feature of practical ML genetic algorithms.

Key Contributions

This paper presents research in the following areas:

• cs.NE
• cs.AI
• cs.LG

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.NE.

Authors

• Anna Brandenberger
• Ilan Doron-Arad
• Elchanan Mossel

Paper Information

• arXiv ID: 2606.12279v1
• Categories: cs.NE, cs.AI, cs.LG
• Published: June 10, 2026
• PDF: Download PDF