Professor John McCall's Outputs (112)

Simultaneous meta-data and meta-classifier selection in multiple classifier system. (2019)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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..

On the definition of dynamic permutation problems under landscape rotation. (2019)
Presentation / Conference Contribution
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..

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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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..

Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem. (2017)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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..

Predictive planning with neural networks. (2016)
Presentation / Conference Contribution
AINSLIE, R., MCCALL, J., SHAKYA, S. and OWUSU, G. 2016. Predictive planning with neural networks. In Proceedings of the International joint conference on neural networks (IJCNN), 24-29 July 2016, Vancouver, Canada. Piscataway: IEEE [online], pages 2110-2117. Available from: https://doi.org/10.1109/IJCNN.2016.7727460

Critical for successful operations of service industries, such as telecoms, utility companies and logistic companies, is the service chain planning process. This involves optimizing resources against expected demand to maximize the utilization and mi... Read More about Predictive planning with neural networks..

BPGA-EDA for the multi-mode resource constrained project scheduling problem. (2016)
Presentation / Conference Contribution
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..

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)
Data
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].