All Outputs (10)

An analysis of indirect optimisation strategies for scheduling. (2018)
Conference Proceeding
NEAU, C., REGNIER-COUDERT, O. and MCCALL, J. 2018. An analysis of indirect optimisation strategies for scheduling. In Proceedings of Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article ID 8477967. Available from: https://doi.org/10.1109/CEC.2018.8477967

By incorporating domain knowledge, simple greedy procedures can be defined to generate reasonably good solutions to many optimisation problems. However, such solutions are unlikely to be optimal and their quality often depends on the way the decision... Read More about An analysis of indirect optimisation strategies for scheduling..

Performance analysis of GA and PBIL variants for real-world location-allocation problems. (2018)
Conference Proceeding
ANKRAH, R., REGNIER-COUDERT, O., MCCALL, J., CONWAY, A. and HARDWICK, A. 2018. Performance analysis of GA and PBIL variants for real-world location-allocation problems. In Proceedings of the 2018 IEEE congress on evolutionary computation (CEC 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8477727. Available from: https://doi.org/10.1109/CEC.2018.8477727

The Uncapacitated Location-Allocation problem (ULAP) is a major optimisation problem concerning the determination of the optimal location of facilities and the allocation of demand to them. In this paper, we present two novel problem variants of Non-... Read More about Performance analysis of GA and PBIL variants for real-world location-allocation problems..

Effective and efficient estimation of distribution algorithms for permutation and scheduling problems. (2018)
Thesis
AYODELE, M. 2018. Effective and efficient estimation of distribution algorithms for permutation and scheduling problems. Robert Gordon University, PhD thesis.

Estimation of Distribution Algorithm (EDA) is a branch of evolutionary computation that learn a probabilistic model of good solutions. Probabilistic models are used to represent relationships between solution variables which may give useful, human-un... Read More about Effective and efficient estimation of distribution algorithms for permutation and scheduling problems..

Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem. (2017)
Conference Proceeding
AYODELE, M., MCCALL, J. and REGNIER-COUDERT, O. 2017. Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem. In Proceedings of the 2017 IEEE congress on evolutionary computation (CEC 2017), 5-8 June 2017, San Sebastian, Spain. New York: IEEE [online], article number 7969491, pages 1579-1586. Available from: https://doi.org/10.1109/CEC.2017.7969491

Multi-Mode Resource Constrained Project Problem (MRCPSP) is a multi-component problem which combines two interacting sub-problems; activity scheduling and mode assignment. Multi-component problems have been of research interest to the evolutionary co... Read More about Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem..

A random key based estimation of distribution algorithm for the permutation flowshop scheduling problem. (2017)
Conference Proceeding
AYODELE, M., MCCALL, J., REGNIER-COUDERT, O. and BOWIE, L. 2017. A random key based estimation of distribution algorithm for the permutation flowshop scheduling problem. In Proceedings of the 2017 IEEE congress on evolutionary computation (CEC 2017), 5-8 June 2017, San Sebastian, Spain. New York: IEEE [online], article number 7969591, pages 2364-2371. Available from: https://doi.org/10.1109/CEC.2017.7969591

Random Key (RK) is an alternative representation for permutation problems that enables application of techniques generally used for continuous optimisation. Although the benefit of RKs to permutation optimisation has been shown, its use within Estima... Read More about A random key based estimation of distribution algorithm for the permutation flowshop scheduling problem..

RK-EDA: a novel random key based estimation of distribution algorithm. (2016)
Conference Proceeding
AYODELE, M., MCCALL, J. and REGNIER-COUDERT, O. 2016. RK-EDA: a novel random key based estimation of distribution algorithm. In Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G. and Paechter, B. (eds.) Parallel problem solving from natuture: proceedings of the 14th International parallel problem solving from nature conference (PPSN XIV), 17-21 September 2016, Edinburgh, UK. Lecture notes in computer science, 9921. Cham: Springer [online], pages 849-858. Available from: https://doi.org/10.1007/978-3-319-45823-6_79

The challenges of solving problems naturally represented as permutations by Estimation of Distribution Algorithms (EDAs) have been a recent focus of interest in the evolutionary computation community. One of the most common alternative representation... Read More about RK-EDA: a novel random key based estimation of distribution algorithm..

BPGA-EDA for the multi-mode resource constrained project scheduling problem. (2016)
Conference Proceeding
AYODELE, M., MCCALL, J. and REGNIER-COUDERT, O. 2016. BPGA-EDA for the multi-mode resource constrained project scheduling problem. In Proceedings of the 2016 IEEE congress on evolutionary computation (CEC 2016), 24-29 July 2016, Vancouver, Canada. Piscataway, NJ: IEEE [online], article number 7744222, pages 3417-3424. Available from: https://doi.org/10.1109/CEC.2016.7744222

The Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) has been of research interest for over two decades. The problem is composed of two interacting sub problems: mode assignment and activity scheduling. These problems cannot be sol... Read More about BPGA-EDA for the multi-mode resource constrained project scheduling problem..

Truck and trailer scheduling in a real world, dynamic and heterogeneous context. (2016)
Journal Article
REGNIER-COUDERT, O., MCCALL, J., AYODELE, M. and ANDERSON, S. 2016. Truck and trailer scheduling in a real world, dynamic and heterogeneous context. Transportation research, part E: logistics and transportation review [online], 93, pages 389-408. Available from: https://doi.org/10.1016/j.tre.2016.06.010

We present a new variant of the Vehicle Routing Problem based on a real industrial scenario. This VRP is dynamic and heavily constrained and uses time-windows, a heterogeneous vehicle fleet and multiple types of job. A constructive solver is develope... Read More about Truck and trailer scheduling in a real world, dynamic and heterogeneous context..

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..

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..