Genetic Algorithms with Optimization Guided Operators
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 m...
| Authors | Anna Brandenberger, Ilan Doron-Arad, Elchanan Mossel |
|---|---|
| Primary category | cs.NE |
| Published | 10 June 2026 |
| Last updated at source | 25 September 2026 |
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