Meta-optimization of Quantum-Inspired Evolutionary Algorithm

In this paper, a meta-optimization algorithm, based on Local Unimodal Sampling (LUS), has been applied to tune selected parameters of QuantumInspired Evolutionary Algorithm for numerical optimization problems coded in real numbers. Tuning of the following two parameters has been considered: crossover rate and contraction factor. Performance landscapes of the algorithm meta-fitness have been approximated and numerical experiments have been conducted on four high-dimensional optimization problems. The tuned algorithm outperformed the algorithm with initially proposed parameters in the conducted experiments.