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Learning and Searching Pseudo-Boolean Surrogate Functions from Small Samples

Swingler K (2019) Learning and Searching Pseudo-Boolean Surrogate Functions from Small Samples. Evolutionary Computation.

When searching for input configurations that optimise the output of a system, it can be useful to build a statistical model of the system being optimised. This is done in approaches such as surrogate model-based optimisation, estimation of distribution algorithms and linkage learning algorithms. This paper presents a method for modelling pseudo-Boolean fitness functions using Walsh bases and an algorithm designed to discover the non-zero coefficients while attempting to minimise the number of fitness function evaluations required. The resulting models reveal linkage structure that can be used to guide a search of the model efficiently. It presents experimental results solving benchmark problems in fewer fitness function evaluations than those reported in the literature for other search methods such as EDAs and linkage learners.

Fitness Function Modelling; Estimation of Distribution Algorithms; Pseudo-Boolean Functions; Linkage Learning; Walsh Decomposition; Mixed Order Hyper Networks; Statistical Machine Learning

Output Status: Forthcoming/Available Online

Evolutionary Computation

Author(s)Swingler, Kevin
Publication date online30/04/2019
Date accepted by journal25/04/2019
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