- author = "George Mweshi and Nelishia Pillay",
- title = "A Grammatical Evolution Approach for the Automated Generation of Perturbative Heuristics",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "2642--2648",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, grammatical evolution",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
doi:10.1109/CEC.2019.8790301",
- size = "7 pages",
- abstract = "Perturbative heuristics, or move operators, are domain specific operators generally used with search techniques, for example the 2-opt operator for the travelling salesman problem. These operators are usually derived manually which is an extremely time consuming task. The study presented in this paper investigates automating this process focussing on deriving perturbation heuristics for discrete optimization problems. This research forms part of a larger initiative aimed at automating the design of machine learning and search techniques. While there has been some research on the automated generation of perturbative operators, this has not been a well researched domain and most work has focussed on recombining existing human derived perturbative heuristics or components thereof together with move acceptance criteria rather than producing new perturbative heuristics from scratch. In this research pertubative heuristics are defined in terms of basic actions and solution components. Gramma",
-
notes = "Also known as \cite{8790301},
IEEE Catalog Number: CFP19ICE-ART",
