Evolvability Metric Estimation by a Parallel Perceptron for On-Line Selection Hyper-Heuristics

Abstract

Online hyper-heuristic selection is a novel and powerful approach to solving complex problems. This approach dynamically selects, based on the state of a given solution, the most promising operator (from a pool of operators) to continue the search process. The dynamic selection is usually based on the analysis of the latest applications of a given operator during actual execution, estimating the potential success of the operator at the current solution state. The estimation can be made by evolvability metrics. Calculating an evolvability metric is computationally expensive since it requires the generation and evaluation of a neighborhood of solutions. This paper aims to estimate the potential success of an operator for a given solution state by using a pre-trained neural network; known as a parallel perceptron. The proposal accelerates the online selection process, allowing us to achieve better performance than hyper-heuristic models, which directly use evolvability functions.

Keywords

• Adaptive algorithm
• optimization
• artificial intelligence
• artificial neural networks
• parallel perceptron