Abstract
We present a hyper-heuristic algorithm for solving combinatorial black-box optimization problems. The algorithm named CMA-VNS stands for a hybrid of variants of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Variable Neighborhood Search (VNS). The framework design and the design profiles of variants of CMA-VNS are introduced to enhance the intensification of searching for conventional CMA-ES solvers. We explain the parameter configuration details, the heuristic profile selection, and the rationale of incorporating machine learning methods during the study. Experimental tests and the results of the first and the second Combinatorial Black-Box Optimization Competitions (CBBOC 2015, 2016) confirmed that CMA-VNS is a competitive hyper-heuristic algorithm.
| Original language | English |
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| Title of host publication | GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion |
| Publisher | Association for Computing Machinery, Inc |
| Pages | 1157-1162 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781450349390 |
| DOIs | |
| Publication status | Published - 15 Jul 2017 |
| Event | 2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017 - Berlin, Germany Duration: 15 Jul 2017 → 19 Jul 2017 |
Conference
| Conference | 2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017 |
|---|---|
| Country/Territory | Germany |
| City | Berlin |
| Period | 15/07/17 → 19/07/17 |
Keywords
- CMA-VNS
- Combinatorial black-box optimization
- Hyperheuristics
- NK-model
ASJC Scopus subject areas
- Software
- Computational Theory and Mathematics
- Computer Science Applications