We test nine different selection hyper-heuristics including an online learning hyper-heuristic on a multi-objective wind farm layout optimisation problem.
In this study, we describe an online learning hyper-heuristic employing a data science technique which is capable of self-improvement via tensor analysis for nurse rostering.
The performance of the proposed learning hyper-heuristic is investigated on the Walking Fish Group test suite which is a common benchmark for multi-objective optimization.
An intensive computational experiment shows, especially in the second class where the new best results are found, the effectiveness of the proposed hyper-heuristics.
We address the important step of determining an effective subset of heuristics in selection hyper-heuristics.
The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.
As compared to other hyper-heuristics based on supervised learning such as decision tree [107, 127], logistic regression [57], support vector machine [129], and artificial neural networks [32, 138], genetic programming (GP) has shown a number of key advantages.
Support vector machine (SVM) is an effective learning technique which constructs an optimal separating hyper plane in the high dimensional feature space.
In this survey, we focus on hyper-heuristics for heuristic generation to fabricate a new heuristic (or meta-heuristic) by combining various small components (normally common statistics/features or operations used in pre-existing heuristics) and these heuristics are trained on a training set and evolved to become more effective.
The results show that the three hyper-heuristics outperformed benchmark dispatching rules and the two-stage hyper-heuristics produced significantly better performance than the other two hyper-heuristics.
