Geometric Surrogate-Based Optimisation for Permutation-Based Problems

Alberto Moraglio, YH Kim, Y Yoon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)


In continuous optimisation, surrogate models (SMs) are used when tackling real-world problems whose candidate solutions are expensive to evaluate. In previous work, we showed that a type of SMs - radial basis function networks (RBFNs) - can be rigorously generalised to encompass combinatorial spaces based in principle on any arbitrarily complex underlying solution representation by extending their natural geometric interpretation from continuous to general metric spaces. This direct approach to representations does not require a vector encoding of solutions, and allows us to use SMs with the most natural representation for the problem at hand. In this work, we apply this framework to combinatorial problems using the permutation representation and report experimental results on the quadratic assignment problem.
Original languageEnglish
Title of host publicationGECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
PublisherAssociation for Computing Machinery
Number of pages2
ISBN (Print)978-1-4503-0690-4
Publication statusPublished - 16 Jul 2011
EventAnnual Conference on Genetic and Evolutionary Computation (GECCO '11), 13th - New York, United States
Duration: 16 Jul 2011 → …


ConferenceAnnual Conference on Genetic and Evolutionary Computation (GECCO '11), 13th
Country/TerritoryUnited States
CityNew York
Period16/07/11 → …


Dive into the research topics of 'Geometric Surrogate-Based Optimisation for Permutation-Based Problems'. Together they form a unique fingerprint.

Cite this