All Outputs (108)

Computational aspects of cellular intelligence and their role in artificial intelligence. (2014)
Thesis
GERRARD, C.E. 2014. Computational aspects of cellular intelligence and their role in artificial intelligence. Robert Gordon University, PhD thesis.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificia... Read More about Computational aspects of cellular intelligence and their role in artificial intelligence..

Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings. (2014)
Presentation / Conference Contribution
CHRISTIE, L.A., MCCALL, J.A.W. and LONIE, D.P. 2014. Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings. In Igel, C. (ed.) Proceedings of the 2014 Genetic and evolutionary computation conference (GECCO 2014): a recombination of the 23rd International conference on genetic algorithms (ICGA-2014), and the 19th Annual genetic programming conference (GP-2014), 12-16 July 2014, Vancouver, Canada. New York: ACM [online], pages 333-340. Available from: https://doi.org/10.1145/2576768.2598240

Problem structure, or linkage, refers to the interaction between variables in a black-box fitness function. Discovering structure is a feature of a range of algorithms, including estimation of distribution algorithms (EDAs) and perturbation methods (... Read More about Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings..

Applications and design of cooperative multi-agent ARN-based systems. (2014)
Journal Article
GERRARD, C.E., MCCALL, J., MACLEOD, C. and COGHILL, G.M. 2015. Applications and design of cooperative multi-agent ARN-based systems. Soft computing [online], 19(6), pages 1581-1594. Available from: https://doi.org/10.1007/s00500-014-1330-9

The Artificial Reaction Network (ARN) is an Artificial Chemistry inspired by Cell Signalling Networks (CSNs). Its purpose is to represent chemical circuitry and to explore the computational properties responsible for generating emergent high-level be... Read More about Applications and design of cooperative multi-agent ARN-based systems..

Multi-objective particle swarm optimisation: methods and applications. (2014)
Thesis
AL MOUBAYED, N. 2014. Multi-objective particle swarm optimisation: methods and applications. Robert Gordon University, PhD thesis.

Solving real life optimisation problems is a challenging engineering venture. Since the early days of research on optimisation it was realised that many problems do not simply have one optimisation objective. This led to the development of multi-obje... Read More about Multi-objective particle swarm optimisation: methods and applications..

The asset replacement problem state of the art. (2013)
Book Chapter
ANSARIPOOR, A.H., OLIVEIRA, F.S. and LIRET, A. 2013. The asset replacement problem state of the art. In Owusu, G., O'Brien, P., McCall, J. and Doherty, N.F. (eds.) Transforming field and service operations: methodologies for successful technology-driven business transformation. Berlin: Springer [online], chapter 14, pages 213-233. Available from: https://doi.org/10.1007/978-3-642-44970-3_14

This book chapter outlines the different modelling approaches for realising sustainable operations of asset replacement and studying the impact of the economic life, the repair-cost limit and comprehensive cost minimisation models. In particular it a... Read More about The asset replacement problem state of the art..

Exploring aspects of cell intelligence with artificial reaction networks. (2013)
Journal Article
GERRARD, C. E., MCCALL, J., COGHILL, G. M. and MACLEOD, C. 2014. Exploring aspects of cell intelligence with artificial reaction networks. Soft computing [online], 18(10), pages 1899-1912. Available from: https://doi.org/10.1007/s00500-013-1174-8

The Artificial Reaction Network (ARN) is a Cell Signalling Network inspired connectionist representation belonging to the branch of A-Life known as Artificial Chemistry. Its purpose is to represent chemical circuitry and to explore computational prop... Read More about Exploring aspects of cell intelligence with artificial reaction networks..

Combining biochemical network motifs within an ARN-agent control system. (2013)
Presentation / Conference Contribution
GERRARD, C.E., MCCALL, J., MACLEOD, C. and COGHILL, G.M. 2013. Combining biochemical network motifs within an ARN-agent control system. In Jin, Y. and Thomas, S.A. (eds.) Proceedings of the 13th UK workshop on computational intelligence (UKCI 2013), 9-11 September 2013, Guildford, UK. New York: IEEE [online], article number 6651281, pages 8-15. Available from: https://doi.org/10.1109/UKCI.2013.6651281

The Artificial Reaction Network (ARN) is an Artificial Chemistry representation inspired by cell signaling networks. The ARN has previously been applied to the simulation of the chemotaxis pathway of Escherichia coli and to the control of limbed robo... Read More about Combining biochemical network motifs within an ARN-agent control system..

Partial structure learning by subset Walsh transform. (2013)
Presentation / Conference Contribution
CHRISTIE, L.A., LONIE, D.P. and MCCALL, J.A.W. 2013. Partial structure learning by subset Walsh transform. In Jin, Y. and Thomas, S.A. (eds.) Proceedings of the 13th UK workshop on computational intelligence (UKCI 2013), 9-11 September 2013, Guildford, UK. New York: IEEE [online], article number 6651297, pages 128-135. Available from: https://doi.org/10.1109/UKCI.2013.6651297

Estimation of distribution algorithms (EDAs) use structure learning to build a statistical model of good solutions discovered so far, in an effort to discover better solutions. The non-zero coefficients of the Walsh transform produce a hypergraph rep... Read More about Partial structure learning by subset Walsh transform..

Artificial chemistry approach to exploring search spaces using artificial reaction network agents. (2013)
Presentation / Conference Contribution
GERRARD, C.E., MCCALL, J., MACLEOD, C. and COGHILL, G.M. 2013. Artificial chemistry approach to exploring search spaces using artificial reaction network agents. In Proceedings of the 2013 IEEE congress on evolutionary computation (CEC 2013), 20-23 June 2013, Cancun, Mexico. New York: IEEE [online], article number 6557702, pages 1201-1208. Available from: https://doi.org/10.1109/CEC.2013.6557702

The Artificial Reaction Network (ARN) is a cell signaling network inspired representation belonging to the branch of A-Life known as Artificial Chemistry. It has properties in common with both AI and Systems Biology techniques including Artificial Ne... Read More about Artificial chemistry approach to exploring search spaces using artificial reaction network agents..

Bayesian network structure learning using characteristic properties of permutation representations with applications to prostate cancer treatment. (2013)
Thesis
REGNIER-COUDERT, O. 2013. Bayesian network structure learning using characteristic properties of permutation representations with applications to prostate cancer treatment. Robert Gordon University, PhD thesis.

Over the last decades, Bayesian Networks (BNs) have become an increasingly popular technique to model data under presence of uncertainty. BNs are probabilistic models that represent relationships between variables by means of a node structure and a s... Read More about Bayesian network structure learning using characteristic properties of permutation representations with applications to prostate cancer treatment..

Experimental user interface design toolkit for interaction research (IDTR). (2013)
Thesis
GOLOVINE, J.C.R.R. 2013. Experimental user interface design toolkit for interaction research (IDTR). Robert Gordon University, PhD thesis.

The research reported and discussed in this thesis represents a novel approach to User Interface evaluation and optimisation through cognitive modelling. This is achieved through the development and testing of a toolkit or platform titled Toolkit for... Read More about Experimental user interface design toolkit for interaction research (IDTR)..

Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence. (2013)
Thesis
FOURNIER, F.A. 2013. Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

This work investigates the use of evolved Bayesian networks learning algorithms based on computational intelligence meta-heuristic algorithms. These algorithms are applied to a new domain provided by the exclusive data, available to this project from... Read More about Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence..

Approximating true relevance model in relevance feedback. (2013)
Thesis
ZHANG, P. 2013. Approximating true relevance model in relevance feedback. Robert Gordon University, PhD thesis.

Relevance is an essential concept in information retrieval (IR) and relevance estimation is a fundamental IR task. It involves not only document relevance estimation, but also estimation of user's information need. Relevance-based language model aims... Read More about Approximating true relevance model in relevance feedback..

Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks. (2012)
Presentation / Conference Contribution
GERRARD, C.E., MCCALL, J., COGHILL, G.M. and MACLEOD, C. 2012. Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks. In Huang, T., Zeng, Z., Li, C. and Leung, C.S. (eds.) Proceedings of the 19th International conference on neural information processing (ICONIP 2012), 12-15 November 2012, Doha, Qatar. Lecture notes in computer science, 7663. Berlin: Springer [online], part I, pages 280-287. Available from: https://doi.org/10.1007/978-3-642-34475-6_34

The Artificial Reaction Network (ARN) is a bio-inspired connectionist paradigm based on the emerging field of Cellular Intelligence. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri N... Read More about Adaptive dynamic control of quadrupedal robotic gaits with artificial reaction networks..

Temporal patterns in artificial reaction networks. (2012)
Presentation / Conference Contribution
GERRARD, C., MCCALL, J., COGHILL, G.M. and MACLEOD, C. 2012. Temporal patterns in artificial reaction networks. In Villa, A.E.P., Duch, W., Érdi, P., Masulli, F. and Palm, G. (eds.) Artificial neural networks and machine learning: proceedings of the 22nd International conference on artificial neural networks (ICANN 2012), 11-14 September 2012, Lausanne, Switzerland. Lecture notes in computer science, 7552. Berlin: Springer [online], part I, pages 1-8. Available from: https://doi.org/10.1007/978-3-642-33269-2_1

The Artificial Reaction Network (ARN) is a bio-inspired connectionist paradigm based on the emerging field of Cellular Intelligence. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri N... Read More about Temporal patterns in artificial reaction networks..

Problem dependent metaheuristic performance in Bayesian network structure learning. (2012)
Thesis
WU, Y. 2012. Problem dependent metaheuristic performance in Bayesian network structure learning. Robert Gordon University, PhD thesis.

Bayesian network (BN) structure learning from data has been an active research area in the machine learning field in recent decades. Much of the research has considered BN structure learning as an optimization problem. However, the finding of optimal... Read More about Problem dependent metaheuristic performance in Bayesian network structure learning..

Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers. (2011)
Journal Article
REGNIER-COUDERT, O., MCCALL, J., LOTHIAN, R., LAM, T., MCCLINTON, S. and N'DOW, J. 2012. Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers. Artificial intelligence in medicine [online], 55(1), pages 25-35. Available from: https://doi.org/10.1016/j.artmed.2011.11.003

Prediction of prostate cancer pathological stage is an essential step in a patient's pathway. It determines the treatment that will be applied further. In current practice, urologists use the pathological stage predictions provided in Partin tables t... Read More about Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers..

Artificial reaction networks. (2011)
Presentation / Conference Contribution
GERRARD, C.E., MCCALL, J., COGHILL, G.M. and MACLEOD, C. 2011. Artificial reaction networks. Presented at the 11th UK workshop on computational intelligence (UKCI 2011), 7-9 September 2011, Manchester, UK.

In this paper we present a novel method of simulating cellular intelligence, the Artificial Reaction Network (ARN). The ARN can be described as a modular S-System, with some properties in common with other Systems Biology and AI techniques, including... Read More about Artificial reaction networks..

A sequence-length sensitive approach to learning biological grammars using inductive logic programming. (2011)
Thesis
MAMER, T. 2011. A sequence-length sensitive approach to learning biological grammars using inductive logic programming. Robert Gordon University, PhD thesis.

This thesis aims to investigate if the ideas behind compression principles, such as the Minimum Description Length, can help us to improve the process of learning biological grammars from protein sequences using Inductive Logic Programming (ILP). Con... Read More about A sequence-length sensitive approach to learning biological grammars using inductive logic programming..

Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm. (2009)
Thesis
BROWNLEE, A.E.I. 2009. Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm. Robert Gordon University, PhD thesis.

A well-known paradigm for optimisation is the evolutionary algorithm (EA). An EA maintains a population of possible solutions to a problem which converges on a global optimum using biologically-inspired selection and reproduction operators. These alg... Read More about Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm..