Design of an efficient hyper-heuristic algorithm CMA-VNS for combinatorial black-box optimization problems

Fan Xue, Qiping Shen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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 languageEnglish
Title of host publicationGECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Number of pages6
ISBN (Electronic)9781450349390
Publication statusPublished - 15 Jul 2017
Event2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017 - Berlin, Germany
Duration: 15 Jul 201719 Jul 2017


Conference2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017


  • Combinatorial black-box optimization
  • Hyperheuristics
  • NK-model

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics
  • Computer Science Applications

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