Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74228
Title: Design of an efficient hyper-heuristic algorithm CMA-VNS for combinatorial black-box optimization problems
Authors: Xue, F
Shen, GQ 
Keywords: CMA-VNS
Combinatorial black-box optimization
Hyperheuristics
NK-model
Issue Date: 2017
Publisher: Association for Computing Machinary
Source: GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017, p. 1157-1162 How to cite?
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.
URI: http://hdl.handle.net/10397/74228
ISBN: 9781450349390
DOI: 10.1145/3067695.3082054
Appears in Collections:Conference Paper

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