Real-Coded Memetic Algorithms with Crossover Hill-Climbing

TLDR

The memetic algorithm enhances global search reliability by promoting high population diversity. The study introduces a real‑coded memetic algorithm that uses crossover hill‑climbing to locally refine solutions generated by genetic operators, aiming to improve accuracy. The algorithm applies crossover hill‑climbing to genetic‑operator solutions and adaptively assigns varying local‑search probabilities to individuals. The algorithm dynamically balances global and local search per problem instance and consistently outperforms existing real‑coded memetic algorithms across diverse benchmarks.

Abstract

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.