Abstract
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 language | English |
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Title of host publication | GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation |
Publisher | Association for Computing Machinery |
Pages | 133-134 |
Number of pages | 2 |
ISBN (Print) | 978-1-4503-0690-4 |
DOIs | |
Publication status | Published - 16 Jul 2011 |
Event | Annual Conference on Genetic and Evolutionary Computation (GECCO '11), 13th - New York, United States Duration: 16 Jul 2011 → … |
Conference
Conference | Annual Conference on Genetic and Evolutionary Computation (GECCO '11), 13th |
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Country/Territory | United States |
City | New York |
Period | 16/07/11 → … |