Professor John McCall's Outputs (66)

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..

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..

Solving the Ising spin glass problem using a bivariate EDA based on Markov random fields. (2006)
Presentation / Conference Contribution
SHAKYA, S.K., MCCALL, J.A.W. and BROWN, D.F. 2006. Solving the Ising spin glass problem using a bivariate EDA based on Markov random fields. In Proceedings of the 2006 IEEE congress on evolutionary computation (CEC 2006), 16-21 July 2006, Vancouver, Canada. New York: IEEE [online], article number 1688408, pages 908-915. Available from: https://doi.org/10.1109/CEC.2006.1688408

Markov Random Field (MRF) modelling techniques have been recently proposed as a novel approach to probabilistic modelling for Estimation of Distribution Algorithms (EDAs). An EDA using this technique was called Distribution Estimation using Markov Ra... Read More about Solving the Ising spin glass problem using a bivariate EDA based on Markov random fields..

Incorporating a metropolis method in a distribution estimation using Markov random field algorithm. (2005)
Presentation / Conference Contribution
SHAKYA, S.K., MCCALL, J.A.W. and BROWN, D.F. 2005. Incorporating a metropolis method in a distribution estimation using Markov random field algorithm. In Proceedings of the 2005 IEEE congress on evolutionary computation (CEC 2005), 2-5 September 2005, Edinburgh, UK. New York: IEEE [online], volume 3, article number 1555017, pages 2576-2583. Available from: https://doi.org/10.1109/CEC.2005.1555017

Markov Random Field (MRF) modelling techniques have been recently proposed as a novel approach to probabilistic modelling for Estimation of Distribution Algorithms (EDAs)[34, 4]. An EDA using this technique, presented in [34], was called Distribution... Read More about Incorporating a metropolis method in a distribution estimation using Markov random field algorithm..

Statistical optimisation and tuning of GA factors. (2005)
Presentation / Conference Contribution
PETROVSKI, A., BROWNLEE, A. and MCCALL, J. 2005. Statistical optimisation and tuning of GA factors. In Proceedings of the 2005 IEEE congress on evolutionary computation (CEC 2005), 2-5 September 2005, Edinburgh, UK. New York: IEEE [online], volume 1, article number 1554759, pages 758-764. Available from: https://doi.org/10.1109/CEC.2005.1554759

This paper presents a practical methodology of improving the efficiency of Genetic Algorithms through tuning the factors significantly affecting GA performance. This methodology is based on the methods of statistical inference and has been successful... Read More about Statistical optimisation and tuning of GA factors..

Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. (2001)
Presentation / Conference Contribution
PETROVSKI, A. and MCCALL, J. 2001. Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. In Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A. and Corne, D. (eds.) Proceedings of the 1st International conference on evolutionary multi-criterion optimization (EMO 2001), 7-9 March 2001, Zurich, Switzerland. Lecture notes in computer science, 1993. Berlin: Springer [online], pages 531-545. Available from: https://doi.org/10.1007/3-540-44719-9_37

The main objectives of cancer treatment in general, and of cancer chemotherapy in particular, are to eradicate the tumour and to prolong the patient survival time. Traditionally, treatments are optimised with only one objective in mind. As a result o... Read More about Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms..