Discrete Gene Crossover Accelerates Solution Discovery in Quality-Diversity Algorithms

2026-02-14

AI Summary

This work addresses the limitation of quality-diversity (QD) algorithms in effectively propagating high-quality genetic modules due to their reliance on incremental mutation, which often leads to premature stagnation in exploration. Inspired by biological meiosis, the authors propose a novel discrete genetic crossover operator that introduces gene-level recombination into the QD framework for the first time. This operator synergistically complements existing variation strategies by preserving elite genetic material while enabling efficient cross-behavioral exploration. By transcending the constraints of traditional incremental mutation, the method significantly improves QD scores, coverage, and peak fitness across three locomotion control tasks. Notably, it demonstrates superior performance gains and sustained diversity maintenance during late-stage optimization, highlighting its capacity to enhance both exploration efficacy and solution quality in complex behavioral spaces.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Quality-Diversity (QD) algorithms aim to discover diverse, high-performing solutions across behavioral niches. However, QD search often stagnates as incremental variation operators struggle to propagate building blocks across large populations. Existing mutation operators rely on gradual variation to solutions, limiting their ability to efficiently explore regions of the search space distant from parent solutions or to spread beneficial genetic material through the population. We propose a mutation operator which augments variation-based operators with discrete, gene-level crossover, enabling rapid recombination of elite genetic material. This crossover mechanism mirrors the biological principle of meiosis and facilitates both the direct transfer of genetic material and the exploration of novel genotype configurations beyond the existing elite hypervolume. We evaluate operators on three locomotion environments, demonstrating improvements in QD score, coverage, and max fitness, with particularly strong performance in later stages of optimization once building blocks have been established in the archive. These results show that the addition of a discrete crossover mutation provides a complementary exploration mechanism that sustains quality-diversity growth beyond the performance demonstrated by existing operators.

Problem

• Quality-Diversity
• search stagnation
• genetic building blocks
• mutation operators
• population diversity

Innovation

• discrete crossover
• quality-diversity algorithms
• gene-level recombination
• meiosis-inspired mutation
• elite hypervolume exploration