Search Results for author: Richard J. Preen

Found 13 papers, 3 papers with code

ACRO: A multi-language toolkit for supporting Automated Checking of Research Outputs

1 code implementation6 Dec 2022 Richard J. Preen, Jim Smith

This paper discusses the development of an open source tool ACRO, (Automatic Checking of Research Outputs) to assist researchers and data governance teams by distinguishing between: research output that is safe to publish; output that requires further analysis; and output that cannot be published because it creates substantial risk of disclosing private data.

Safe machine learning model release from Trusted Research Environments: The AI-SDC package

1 code implementation2 Dec 2022 Jim Smith, Richard J. Preen, Andrew McCarthy, Alba Crespi-Boixader, James Liley, Simon Rogers

We present AI-SDC, an integrated suite of open source Python tools to facilitate Statistical Disclosure Control (SDC) of Machine Learning (ML) models trained on confidential data prior to public release.

Deep Learning with a Classifier System: Initial Results

no code implementations1 Mar 2021 Richard J. Preen, Larry Bull

This article presents the first results from using a learning classifier system capable of performing adaptive computation with deep neural networks.

Handwritten Digit Recognition

Autoencoding with a Classifier System

no code implementations23 Oct 2019 Richard J. Preen, Stewart W. Wilson, Larry Bull

Learning classifier systems (LCS) are a framework for adaptively subdividing input spaces into an ensemble of simpler local approximations that together cover the domain.

Dimensionality Reduction

Towards an Evolvable Cancer Treatment Simulator

no code implementations19 Dec 2018 Richard J. Preen, Larry Bull, Andrew Adamatzky

The use of high-fidelity computational simulations promises to enable high-throughput hypothesis testing and optimisation of cancer therapies.

Evolutionary Algorithms Two-sample testing

Evolutionary n-level Hypergraph Partitioning with Adaptive Coarsening

no code implementations25 Mar 2018 Richard J. Preen, Jim Smith

This article presents a novel memetic algorithm which remains effective on larger initial hypergraphs.

Evolutionary Algorithms hypergraph partitioning

Design Mining Microbial Fuel Cell Cascades

no code implementations18 Oct 2016 Richard J. Preen, Jiseon You, Larry Bull, Ioannis A. Ieropoulos

Microbial fuel cells (MFCs) perform wastewater treatment and electricity production through the conversion of organic matter using microorganisms.

On Design Mining: Coevolution and Surrogate Models

no code implementations29 Jun 2015 Richard J. Preen, Larry Bull

Design mining is the use of computational intelligence techniques to iteratively search and model the attribute space of physical objects evaluated directly through rapid prototyping to meet given objectives.

Attribute

Design Mining Interacting Wind Turbines

no code implementations2 Oct 2014 Richard J. Preen, Larry Bull

The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared.

Evolutionary Algorithms

Toward the Coevolution of Novel Vertical-Axis Wind Turbines

no code implementations13 Aug 2013 Richard J. Preen, Larry Bull

The production of renewable and sustainable energy is one of the most important challenges currently facing mankind.

Towards the Evolution of Vertical-Axis Wind Turbines using Supershapes

1 code implementation18 Apr 2012 Richard J. Preen, Larry Bull

We have recently presented an initial study of evolutionary algorithms used to design vertical-axis wind turbines (VAWTs) wherein candidate prototypes are evaluated under approximated wind tunnel conditions after being physically instantiated by a 3D printer.

3D Shape Representation Evolutionary Algorithms

Discrete Dynamical Genetic Programming in XCS

no code implementations18 Apr 2012 Richard J. Preen, Larry Bull

A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to neural networks.

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

no code implementations26 Jan 2012 Richard J. Preen, Larry Bull

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks.

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