David Corne

Fabio Caraffini, Anna V. Kononova, David Corne

This paper thoroughly investigates a range of popular DE configurations to identify components responsible for the emergence of structural bias - recently identified tendency of the algorithm to prefer some regions of the search space for reasons directly unrelated to the objective function values. Such tendency was already studied in GA and PSO where a connection was established between the strength of structural bias and population sizes and potential weaknesses of these algorithms was highlighted. For DE, this study goes further and extends the range of aspects that can contribute to presence of structural bias by including algorithmic component which is usually overlooked - constraint handling technique. A wide range of DE configurations were subjected to the protocol for testing for bias. Results suggest that triggering mechanism for the bias in DE differs to the one previously found for GA and PSO - no clear dependency on population size exists. Setting of DE parameters is based on a separate study which on its own leads to interesting directions of new research. Overall, DE turned out to be robust against structural bias - only DE/current-to-best/1/bin is clearly biased but this effect is mitigated by the use of penalty constraint handling technique.

Václav Potoček, Alan P. Reynolds, Alessandro Fedrizzi et al.

Quantum hardware continues to advance, yet finding new quantum algorithms - quantum software - remains a challenge, with classically trained computer programmers having little intuition of how computational tasks may be performed in the quantum realm. As such, the idea of developing automated tools for algorithm development is even more appealing for quantum computing than for classical. Here we develop a robust, multi-objective evolutionary search strategy to design quantum circuits 'from scratch', by combining and parameterizing a task-generic library of quantum circuit elements. When applied to 'ab initio' design of quantum circuits for the input/output mapping requirements of the quantum Fourier transform and Grover's search algorithm, it finds textbook circuit designs, along with alternative structures that achieve the same functionality. Exploiting its multi-objective nature, the discovery algorithm can trade off performance measures such as accuracy, circuit width or depth, gate count, or implementability - a crucial requirement for first-generation quantum processors and applications.

David W. Corne, Michael A. Lones

Evolutionary algorithms (EAs) are population-based metaheuristics, originally inspired by aspects of natural evolution. Modern varieties incorporate a broad mixture of search mechanisms, and tend to blend inspiration from nature with pragmatic engineering concerns; however, all EAs essentially operate by maintaining a population of potential solutions and in some way artificially 'evolving' that population over time. Particularly well-known categories of EAs include genetic algorithms (GAs), Genetic Programming (GP), and Evolution Strategies (ES). EAs have proven very successful in practical applications, particularly those requiring solutions to combinatorial problems. EAs are highly flexible and can be configured to address any optimization task, without the requirements for reformulation and/or simplification that would be needed for other techniques. However, this flexibility goes hand in hand with a cost: the tailoring of an EA's configuration and parameters, so as to provide robust performance for a given class of tasks, is often a complex and time-consuming process. This tailoring process is one of the many ongoing research areas associated with EAs.

Osama Khalifa, David Wolfe Corne, Mike Chantler

Hyper-parameters play a major role in the learning and inference process of latent Dirichlet allocation (LDA). In order to begin the LDA latent variables learning process, these hyper-parameters values need to be pre-determined. We propose an extension for LDA that we call 'Latent Dirichlet allocation Gibbs Newton' (LDA-GN), which places non-informative priors over these hyper-parameters and uses Gibbs sampling to learn appropriate values for them. At the heart of LDA-GN is our proposed 'Gibbs-Newton' algorithm, which is a new technique for learning the parameters of multivariate Polya distributions. We report Gibbs-Newton performance results compared with two prominent existing approaches to the latter task: Minka's fixed-point iteration method and the Moments method. We then evaluate LDA-GN in two ways: (i) by comparing it with standard LDA in terms of the ability of the resulting topic models to generalize to unseen documents; (ii) by comparing it with standard LDA in its performance on a binary classification task.

Anna V. Kononova, David W. Corne, Philippe De Wilde et al.

Challenging optimisation problems are abundant in all areas of science. Since the 1950s, scientists have developed ever-diversifying families of black box optimisation algorithms designed to address any optimisation problem, requiring only that quality of a candidate solution is calculated via a fitness function specific to the problem. For such algorithms to be successful, at least three properties are required: an effective informed sampling strategy, that guides generation of new candidates on the basis of fitnesses and locations of previously visited candidates; mechanisms to ensure efficiency, so that same candidates are not repeatedly visited; absence of structural bias, which, if present, would predispose the algorithm towards limiting its search to some regions of solution space. The first two of these properties have been extensively investigated, however the third is little understood. In this article we provide theoretical and empirical analyses that contribute to the understanding of structural bias. We prove a theorem concerning dynamics of population variance in the case of real-valued search spaces. This reveals how structural bias can manifest as non-uniform clustering of population over time. Theory predicts that structural bias is exacerbated with increasing population size and problem difficulty. These predictions reveal two previously unrecognised aspects of structural bias. Respectively, increasing population size, though ostensibly promoting diversity, will magnify any inherent structural bias, and effects of structural bias are more apparent when faced with difficult problems. Our theoretical result also suggests that two commonly used approaches to enhancing exploration, increasing population size and increasing disruptiveness of search operators, have quite distinct implications in terms of structural bias.