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Genetic algorithms have proved their efficiency with many hard optimization problems, but in order to achive the best results they must be fine-tuned. One such method of fine-tuning is a synthesis of new genetic operators. Hyper-heuristics represent search techniques that can be used for automating the process of selecting or generating simpler heuristics with the aim of designing new metaheuristic algorithms. In this study, we have proposed a new hyper-heuristic based on genetic programming for the automated synthesis of a selection operator in genetic algorithms. Black-Box Optimization Benchmarking is used as a training set for the genetic programming algorithm and as a test set for estimating the generalization ability of a synthesized selection operator. The results of numerical experiments are presented and discussed. The experiments have shown that the proposed approach can be used for designing new selection operators that outperform standard selection operators on average with new, previously unseen instances of hard black-box optimization

Genetic Programming Hyper-heuristic for the Automated Synthesis of Selection Operators in Genetic Algorithms