Computer Science Seminar Series

Abstract

This talk introduces Mixed Order Hyper Networks (MOHNS), a machine learning technique that can be used for regression, as a generative network or as a surrogate fitness function in optimisation tasks. MOHNS can be considered a type of high order neural network with similarities to both MLPs and Hopfield networks, but with qualities lacking in both of those architectures. This work concentrates on binary MOHNS, which map vectors of binary variables onto a real valued output. There are convex cost functions available for training MOHNS, meaning that there are no local minima to trap the training algorithm. Binary MOHNs represent a basis set, meaning that any binary function can be modelled to arbitrary accuracy. The structure of connectivity of a learned MOHN is, to some extent, human readable, allowing insights into the underlying function. The interpretable nature of the MOHN architecture also makes it an interesting choice as a surrogate fitness function for metaheuristic optimisation tasks. Experiments have shown that several of the standard benchmark combinatorial fitness functions can be modelled in far fewer evaluations than are required to find an optimal solution by other search heuristics. When sampling from a noise free fitness function, the number of fitness evaluations required to build a model is equal to the number of parameters in the model, allowing a lower bound of required evaluations to be defined for a given function. This talk will describe the structure and learning algorithms for MOHNS and then give some examples of their use as surrogate fitness function models.

Host: Phil Bartie

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