All Outputs (97)

Deep heterogeneous ensemble. (2019)
Journal Article
NGUYEN, T.T., DANG, M.T., PHAM, T.D., DAO, L.P., LUONG, A.V., MCCALL, J. and LIEW, A.W.C. 2019. Deep heterogeneous ensemble. Australian journal of intelligent information processing systems [online], 16(1): special issue on neural information processing: proceedings of the 26th International conference on neural information processing (ICONIP 2019), 12-15 December 2019, Sydney, Australia, pages 1-9. Available from: http://ajiips.com.au/papers/V16.1/v16n1_5-13.pdf

In recent years, deep neural networks (DNNs) have emerged as a powerful technique in many areas of machine learning. Although DNNs have achieved great breakthrough in processing images, video, audio and text, it also has some limitations... Read More about Deep heterogeneous ensemble..

Generation and optimisation of real-world static and dynamic location-allocation problems with application to the telecommunications industry. (2019)
Thesis
ANKRAH, R.B. 2019. Generation and optimisation of real-world static and dynamic location-allocation problems with application to the telecommunications industry. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

The location-allocation (LA) problem concerns the location of facilities and the allocation of demand, to minimise or maximise a particular function such as cost, profit or a measure of distance. Many formulations of LA problems have been presented i... Read More about Generation and optimisation of real-world static and dynamic location-allocation problems with application to the telecommunications industry..

Evolving an optimal decision template for combining classifiers. (2019)
Conference Proceeding
NGUYEN, T.T., LUONG, A.V., DANG, M.T., DAO, L.P., NGUYEN, T.T.T., LIEW, A.W.-C. and MCCALL, J. 2019. Evolving an optimal decision template for combining classifiers. In Gedeon, T., Wong, K.W. and Lee, M. (eds.) Neural information processing: proceedings of the 26th International conference on neural information processing (ICONIP 2019), 12-15 December 2019, Sydney, Australia. Part I. Lecture notes in computer science, 11953. Cham: Springer [online], pages 608-620. Available from: https://doi.org/10.1007/978-3-030-36708-4_50

In this paper, we aim to develop an effective combining algorithm for ensemble learning systems. The Decision Template method, one of the most popular combining algorithms for ensemble systems, does not perform well when working on certain datasets l... Read More about Evolving an optimal decision template for combining classifiers..

Ensemble selection based on classifier prediction confidence. (2019)
Journal Article
NGUYEN, T.T., LUONG, A.V., DANG, M.T., LIEW, A.W.-C. and MCCALL, J. 2020. Ensemble selection based on classifier prediction confidence. Pattern recognition [online], 100, article ID 107104. Available from: https://doi.org/10.1016/j.patcog.2019.107104

Ensemble selection is one of the most studied topics in ensemble learning because a selected subset of base classifiers may perform better than the whole ensemble system. In recent years, a great many ensemble selection methods have been introduced.... Read More about Ensemble selection based on classifier prediction confidence..

Introducing the dynamic customer location-allocation problem. (2019)
Conference Proceeding
ANKRAH, R., LACROIX, B., MCCALL, J., HARDWICK, A. and CONWAY, A. 2019. Introducing the dynamic customer location-allocation problem. In Proceedings of the 2019 Institute of Electrical and Electronics Engineers (IEEE) Congress on evolutionary computation (IEEE CEC 2019), 10-13 June 2019, Wellington, NZ. Piscataway: IEEE [online], pages 3157-3164. Available from: https://doi.org/10.1109/CEC.2019.8790150

In this paper, we introduce a new stochastic Location-Allocation Problem which assumes the movement of customers over time. We call this new problem Dynamic Customer Location-Allocation Problem (DC-LAP). The problem is based on the idea that customer... Read More about Introducing the dynamic customer location-allocation problem..

On the definition of dynamic permutation problems under landscape rotation. (2019)
Conference Proceeding
ALZA, J., BARTLETT, M., CEBERIO, J. and MCCALL, J. 2019. On the definition of dynamic permutation problems under landscape rotation. In López-Ibáñez, M. (ed.) Proceedings of the 2019 Genetic and evolutionary computation conference companion (GECCO 2019), 13-17 July 2019, Prague, Czech Republic. New York: ACM [online], pages 1518-1526. Available from: https://doi.org/10.1145/3319619.3326840

Dynamic optimisation problems (DOPs) are optimisation problems that change over time. Typically, DOPs have been defined as a sequence of static problems, and the dynamism has been inserted into existing static problems using different techniques. In... Read More about On the definition of dynamic permutation problems under landscape rotation..

Simultaneous meta-data and meta-classifier selection in multiple classifier system. (2019)
Conference Proceeding
NGUYEN, T.T., LUONG, A.V., NGUYEN, T.M.V., HA, T.S., LIEW, A.W.-C. and MCCALL, J. 2019. Simultaneous meta-data and meta-classifier selection in multiple classifier system. In López-Ibáñez, M. (ed.) Proceedings of the 2019 Genetic and evolutionary computation conference (GECCO ’19), 13-17 July 2019, Prague, Czech Republic. New York: ACM [online], pages 39-46. Available from: https://doi.org/10.1145/3321707.3321770

In ensemble systems, the predictions of base classifiers are aggregated by a combining algorithm (meta-classifier) to achieve better classification accuracy than using a single classifier. Experiments show that the performance of ensembles significan... Read More about Simultaneous meta-data and meta-classifier selection in multiple classifier system..

Limitations of benchmark sets and landscape features for algorithm selection and performance prediction. (2019)
Conference Proceeding
LACROIX, B. and MCCALL, J. 2019. Limitations of benchmark sets and landscape features for algorithm selection and performance prediction. In López-Ibáñe, M. (ed.) Proceedings of the 2019 Genetic and evolutionary computation conference (GECCO 2019) companion, 13-17 July 2019, Prague, Czech Republic. New York: Association for Computing Machinery [online], pages 261-262. Available from: https://doi.org/10.1145/3319619.3322051

Benchmark sets and landscape features are used to test algorithms and to train models to perform algorithm selection or configuration. These approaches are based on the assumption that algorithms have similar performances on problems with similar fea... Read More about Limitations of benchmark sets and landscape features for algorithm selection and performance prediction..

Multi-label classification via incremental clustering on an evolving data stream. (2019)
Journal Article
NGUYEN, T.T., DANG, M.T., LUONG, A.V., LIEW, A. W.-C., LIANG, T. and MCCALL, J. 2019. Multi-label classification via incremental clustering on an evolving data stream. Pattern recognition [online], 95, pages 96-113. Available from: https://doi.org/10.1016/j.patcog.2019.06.001

With the advancement of storage and processing technology, an enormous amount of data is collected on a daily basis in many applications. Nowadays, advanced data analytics have been used to mine the collected data for useful information and make pred... Read More about Multi-label classification via incremental clustering on an evolving data stream..

A holistic metric approach to solving the dynamic location-allocation problem. (2018)
Conference Proceeding
ANKRAH, R., LACROIX, B., MCCALL, J., HARDWICK, A. and CONWAY, A. 2018. A holistic metric approach to solving the dynamic location-allocation problem. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 433-439. Available from: https://doi.org/10.1007/978-3-030-04191-5_35

In this paper, we introduce a dynamic variant of the Location-Allocation problem: Dynamic Location-Allocation Problem (DULAP). DULAP involves the location of facilities to service a set of customer demands over a defined horizon. To evaluate a soluti... Read More about A holistic metric approach to solving the dynamic location-allocation problem..

Tactical plan optimisation for large multi-skilled workforces using a bi-level model. (2018)
Conference Proceeding
AINSLIE, R., MCCALL, J., SHAKYA, S. and OWUSU, G. 2018. Tactical plan optimisation for large multi-skilled workforces using a bi-level model. 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 8477701. Available from: https://doi.org/10.1109/CEC.2018.8477701

The service chain planning process is a critical component in the operations of companies in the service industry, such as logistics, telecoms or utilities. This process involves looking ahead over various timescales to ensure that available capacity... Read More about Tactical plan optimisation for large multi-skilled workforces using a bi-level model..

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..

Iterated racing algorithm for simulation-optimisation of maintenance planning. (2018)
Conference Proceeding
LACROIX, B., MCCALL, J. and LONCHAMPT, J. 2018. Iterated racing algorithm for simulation-optimisation of maintenance planning. 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 8477843. Available from: https://doi.org/10.1109/CEC.2018.8477843

The purpose of this paper is two fold. First, we present a set of benchmark problems for maintenance optimisation called VMELight. This model allows the user to define the number of components in the system to maintain and a number of customisable pa... Read More about Iterated racing algorithm for simulation-optimisation of maintenance planning..

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..

Predicting service levels using neural networks. (2017)
Conference Proceeding
AINSLIE, R., MCCALL, J., SHAKYA, S. and OWUSU, G. 2017. Predicting service levels using neural networks. In Bramer, M. and Petridis, M. (eds.) Artificial intelliegence XXXIV: proceedings of the 37th SGAI International innovative techniques and applications of artifical intelligence conference 2017 (AI 2017), 12-14 December 2017, Cambridge, UK. Lecture notes in computer science, 10630. Cham: Springer [online], pages 411-416. Available from: https://doi.org/10.1007/978-3-319-71078-5_35

In this paper we present a method to predict service levels in utility companies, giving them advanced visibility of expected service outcomes and helping them to ensure adherence to service level agreements made to their clients. Service level adher... Read More about Predicting service levels using neural networks..

Simulation and optimisation of the separation process in offshore oil and gas platforms. (2017)
Thesis
VELESHKI, S. 2017. Simulation and optimisation of the separation process in offshore oil and gas platforms. Robert Gordon University, MRes thesis.

Hydrocarbon separation in offshore oil and gas platforms is the process that transforms extracted crude oil into transportable oil and gas. Temperatures and pressures of the separation system can be adjusted to modify the separation of the hydrocarbo... Read More about Simulation and optimisation of the separation process in offshore oil and gas platforms..

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..

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..

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..