Evolutionary Generative Optimization: Towards Fully Data-Driven Evolutionary Optimization via Generative Learning

2025-08-01

AI Summary

Existing data-driven evolutionary algorithms still rely on handcrafted heuristics, suffering from limited generality and insufficient automation. Method: This paper proposes EvoGO—the first fully data-driven generative evolutionary optimization framework—decoupling optimization into three stages: training-data construction, generative model training, and population generation. EvoGO replaces conventional mutation and crossover operators entirely with a customized generative model. By constructing diversity-enhanced pairwise training data, it enables parallel, zero-evaluation-cost population generation. Contribution/Results: On diverse benchmark tasks—including continuous, discrete, and combinatorial optimization—EvoGO achieves convergence within only 10 generations, significantly outperforming classical evolutionary algorithms, Bayesian optimization, and reinforcement learning–based methods. Empirical results demonstrate its superior efficiency, generalizability across problem domains, and strong scalability to high-dimensional and large-population settings.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent advances in data-driven evolutionary algorithms (EAs) have demonstrated the potential of leveraging data to improve optimization accuracy and adaptability. Nevertheless, most existing approaches remain dependent on handcrafted heuristics, which limits their generality and automation. To address this challenge, we propose Evolutionary Generative Optimization (EvoGO), a fully data-driven framework empowered by generative learning. EvoGO streamlines the evolutionary optimization process into three stages: data preparation, model training, and population generation. The data preparation stage constructs a pairwise dataset to enrich training diversity without incurring additional evaluation costs. During model training, a tailored generative model learns to transform inferior solutions into superior ones. In the population generation stage, EvoGO replaces traditional reproduction operators with a scalable and parallelizable generative mechanism. Extensive experiments on numerical benchmarks, classical control problems, and high-dimensional robotic tasks demonstrate that EvoGO consistently converges within merely 10 generations and significantly outperforms a wide spectrum of optimization approaches, including traditional EAs, Bayesian optimization, and reinforcement learning based methods. Source code will be made publicly available.

Problem

• Develop fully data-driven evolutionary optimization without handcrafted heuristics
• Replace traditional EA operators with generative learning mechanisms
• Improve optimization accuracy and adaptability across diverse tasks

Innovation

• Fully data-driven framework via generative learning
• Pairwise dataset enriches training diversity efficiently
• Generative model replaces traditional reproduction operators