Meta-MOGA: Meta-learning Multi-Objective Genetic Algorithm

In the field of single objective optimization algorithms, learned evolutionary algorithms have achieved success in obtaining better performance than human-designed strategies. However, these learnable evolutionary algorithms are only applicable to single-objective optimization and cannot be applied...

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Veröffentlicht in:	2025 IEEE Congress on Evolutionary Computation (CEC) S. 1 - 4
Hauptverfasser:	Li, Tianyu, Wu, Kai, Li, Xiaobin, Teng, Xiangyi, Liu, Jing
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 08.06.2025
Schlagworte:	Evolutionary computation Evolutionary multi-objective optimization algorithms Genetic algorithms Meta-learning Metalearning Multi-objective optimization Multilayer perceptrons Optimization
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Zusammenfassung:	In the field of single objective optimization algorithms, learned evolutionary algorithms have achieved success in obtaining better performance than human-designed strategies. However, these learnable evolutionary algorithms are only applicable to single-objective optimization and cannot be applied to multi-objective optimization problems. In this study, we parameterize the mutation and crossover operators using the multi-head self-attention and the selection operator using a lightweight multilayer perceptron. We utilize the evolution strategy to train their parameters across multiple multi-objective optimization problems, resulting in the development of the Meta-Learned Multi-Objective Genetic Algorithm (Meta-MOGA). We compare Meta-MOGA with other multi-objective evolutionary algorithms on various test problems and evaluate its performance on untrained MOPs. The results demonstrate that our Meta-MOGA exhibits potential and generalizability.
DOI:	10.1109/CEC65147.2025.11043112