Abstract: Many-objective optimization problems are challenging and time-consuming, especially when the number of objectives is large. In this paper, an evolutionary algorithm inspired by quantum ...

Abstract: Multimodal multi-objective optimization problems (MMOPs) are prevalent in real-world applications and have emerged as a significant research focus in evolutionary computation. Unlike ...

OpenEvolve uses large language models as mutation and crossover operators inside an evolutionary algorithm loop. Describe a problem, provide test cases, and let AI evolve increasingly better solutions ...