Geometric Generalisation of Surrogate Model-Based Optimisation to Combinatorial and Program Spaces. (29th April 2014)

• Record Type: Journal Article
• Authors: Kim, Yong-Hyuk; Moraglio, Alberto; Kattan, Ahmed; Yoon, Yourim
• Other Names: Yu Ker-Wei Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-04-29
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/184540
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Surrogate models (SMs) can profitably be employed, often in conjunction with evolutionary algorithms, in optimisation in which it is expensive to test candidate solutions. The spatial intuition behind SMs makes them naturally suited to continuous problems, and the only combinatorial problems that have been previously addressed are those with solutions that can be encoded as integer vectors. We show how radial basis functions can provide a generalised SM for combinatorial problems which have a geometric solution representation, through the conversion of that representation to a different metric space. This approach allows an SM to be cast in a natural way for the problem at hand, without ad hoc adaptation to a specific representation. We test this adaptation process on problems involving binary strings, permutations, and tree-based genetic programs.