All Outputs (93)

The role of Walsh structure and ordinal linkage in the optimisation of pseudo-Boolean functions under monotonicity invariance. (2016)
Thesis
CHRISTIE, L.A. 2016. The role of Walsh structure and ordinal linkage in the optimisation of pseudo-Boolean functions under monotonicity invariance. Robert Gordon University, PhD thesis.

Optimisation heuristics rely on implicit or explicit assumptions about the structure of the black-box fitness function they optimise. A review of the literature shows that understanding of structure and linkage is helpful to the design and analysis o... Read More about The role of Walsh structure and ordinal linkage in the optimisation of pseudo-Boolean functions under monotonicity invariance..

Generating easy and hard problems using the proximate optimality principle. [Dataset] (2015)
Dataset
MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2015. Generating easy and hard problems using the proximate optimality principle. [Dataset]

These data were gathered to investigate the hypothesis that coherent functions will be easy and anti-coherent functions will be hard for a hillclimber. We generated 10 coherent functions for each length on bit-strings of length 6-100 and the same num... Read More about Generating easy and hard problems using the proximate optimality principle. [Dataset].

Generating easy and hard problems using the proximate optimality principle. (2015)
Conference Proceeding
MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2015. Generating easy and hard problems using the proximate optimality principle. In Silva, S. (ed.) Proceedings of the companion publication of the 2015 annual conference on genetic and evolutionary computation (GECCO Companion '15), 11-15 July 2015, Madrid, Spain. New York: ACM [online], pages 767-768. Available from: https://doi.org/10.1145/2739482.2764890

We present an approach to generating problems of variable difficulty based on the well-known Proximate Optimality Principle (POP), often paraphrased as similar solutions have similar fitness. We explore definitions of this concept in terms of metrics... Read More about Generating easy and hard problems using the proximate optimality principle..

Autonomic behavioural framework for structural parallelism over heterogeneous multi-core systems. (2015)
Thesis
GOLI, M. 2015. Autonomic behavioural framework for structural parallelism over heterogeneous multi-core systems. Robert Gordon University, PhD thesis.

With the continuous advancement in hardware technologies, significant research has been devoted to design and develop high-level parallel programming models that allow programmers to exploit the latest developments in heterogeneous multi-core/many-co... Read More about Autonomic behavioural framework for structural parallelism over heterogeneous multi-core systems..

Adaptive heterogeneous parallelism for semi-empirical lattice dynamics in computational materials science. (2015)
Thesis
GARBA, M. 2015. Adaptive heterogeneous parallelism for semi-empirical lattice dynamics in computational materials science. Robert Gordon University, PhD thesis.

With the variability in performance of the multitude of parallel environments available today, the conceptual overhead created by the need to anticipate runtime information to make design-time decisions has become overwhelming. Performance-critical a... Read More about Adaptive heterogeneous parallelism for semi-empirical lattice dynamics in computational materials science..

Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems. (2015)
Conference Proceeding
BROWNLEE, A.E.I., MCCALL, J.A.W. and CHRISTIE, L.A. 2015. Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems. In Proceedings of the 2015 IEEE congress on evolutionary computation (CEC 2015), 25-28 May 2015, Sendai, Japan. Piscataway, NJ: IEEE [online], pages 2066-2073. Available from: https://doi.org/10.1109/CEC.2015.7257139

Metaheuristics assume some kind of coherence between decision and objective spaces. Estimation of Distribution algorithms approach this by constructing an explicit probabilistic model of high fitness solutions, the structure of which is intended to r... Read More about Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems..

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)
Conference Proceeding
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..

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..

Partial structure learning by subset Walsh transform. (2013)
Conference Proceeding
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..

Combining biochemical network motifs within an ARN-agent control system. (2013)
Conference Proceeding
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..

Artificial chemistry approach to exploring search spaces using artificial reaction network agents. (2013)
Conference Proceeding
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..

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)..

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..

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)
Conference Proceeding
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)
Conference Proceeding
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..