Publications

The Artificial Bee Colony (ABC) is a recently introduced swarm intelligence algorithm for optimization, that has previously been applied successfully to the training of neural networks. This paper explores more carefully the performance of the ABC...
The Artificial Bee Colony (ABC) is a swarm intelligence algorithm for optimization that has previously been applied to the training of neural networks. This paper examines more carefully the performance of the ABC algorithm for optimizing the...
This paper empirically studies basic properties of the fitness landscape of random instances of number partitioning problem, with a focus on how these properties change with the phase transition. The properties include number of local and global...
This paper investigates the relation between the distribution of the weights and the number of local optima in the Number Partitioning Problem (NPP). The number of local optima in the 1-bit flip landscape was found to be strongly and negatively...
This paper studies two landscapes of different instances of the 0-1 knapsack problem. The instances are generated randomly from varied weight distributions. We show that the variation of the weights can be used to guide the selection of the most...
We evaluate the performance of estimating the number of local optima by estimating their proportion in the search space using simple random sampling (SRS). The performance of this method is compared against that of the jackknife method. The methods...
Game semantics is a powerful method of semantic analysis for programming languages. It gives mathematically accurate models ("fully abstract") for a wide variety of programming languages. Game semantic models are combinatorial characterisations of...
The parameter explosion problem is a crucial bottleneck in modelling gene regulatory networks (GRNs), limiting the size of models that can be optimised to experimental data. By discretising state, but not time, Boolean delay equations (BDEs) provide...
 Efficient robust optimisation methods exploit the search history when evaluating a new solution by using information from previously visited solutions that fall in the new solution's uncertainty neighbourhood. We propose a full exploitation of the...
We propose a framework for estimating the quality of solutions in a robust optimisation setting by utilising samples from the search history and using MC sampling to approximate a Voronoi tessellation. This is used to determine a new point in the...
Robust and multi-modal optimisation are two important topics that have received significant attention from the evolutionary computation community over the past few years. However, the two topics have usually been investigated independently and there...
This article presents an exploratory landscape analysis of three NP-hard combinatorial optimisation problems: the number partitioning problem, the binary knapsack problem, and the quadratic binary knapsack problem. In the article, we examine...
There are situations where the need for optimisation with a global precision tolerance arises - for example, due to measurement, numerical or evaluation errors in the objective function. In such situations, a global tolerance ϵ > 0 can be...
We present a prototypical multi-modal optimisation problem from the systems biology domain -- tuning the kinetic parameters of a reduced-order gene regulatory network (GRN) model to obtain optimal fits to gene expression timeseries. After...
We are proposing a method for identifying whether the observed behaviour of a function at an interface is consistent with the typical behaviour of a particular programming language. This is a challenging problem with significant potential...