All Outputs (56)

Toward an ensemble of object detectors. (2020)
Conference Proceeding
DANG, T., NGUYEN, T.T. and MCCALL, J. 2020. Toward an ensemble of object detectors. In Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H. and King, I. (eds.) Neural information processing: proceedings of 27th International conference on neural information processing 2020 (ICONIP 2020), part 5. Communications in computer and information science, 1333. Cham: Springer [online], pages, 458-467. Available from: https://doi.org/10.1007/978-3-030-63823-8_53

The field of object detection has witnessed great strides in recent years. With the wave of deep neural networks (DNN), many breakthroughs have achieved for the problems of object detection which previously were thought to be difficult. However, ther... Read More about Toward an ensemble of object detectors..

A homogeneous-heterogeneous ensemble of classifiers. (2020)
Conference Proceeding
LUONG, A.V., VU, T.H., NGUYEN, P.M., VAN PHAM, N., MCCALL, J., LIEW, A.W.-C. and NGUYEN, T.T. 2020. A homogeneous-heterogeneous ensemble of classifiers. In Yang, H., Pasupa, K., Leung, A.C.-S., Kwok, J.T., Chan, J.H. and King, I. (eds.) Neural information processing: proceedings of 27th International conference on neural information processing 2020 (ICONIP 2020), part 5. Communications in computer and information science, 1333. Cham: Springer [online], pages, 251-259. Available from: https://doi.org/10.1007/978-3-030-63823-8_30

In this study, we introduce an ensemble system by combining homogeneous ensemble and heterogeneous ensemble into a single framework. Based on the observation that the projected data is significantly different from the original data as well as each ot... Read More about A homogeneous-heterogeneous ensemble of classifiers..

Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems. (2020)
Conference Proceeding
ZAVOIANU, A.-C., LACROIX, B. and MCCALL, J. 2020. Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems. In Bäck, T., Preuss, M., Deutz, A., Wang, H., Doerr, C., Emmerich, M. and Trautmann, H. (eds.) Parallel problem solving from nature: PPSN XVI: proceedings of the 16th Parallel problem solving from nature international conference (PPSN 2020), 5-9 September 2020, Leiden, The Netherlands. Lecture notes in computer science, 12269. Cham; Springer, part 1, pages 287-300. Available from: https://doi.org/10.1007/978-3-030-58112-1_20

We propose a new class of multi-objective benchmark problems on which we analyse the performance of four well established multi-objective evolutionary algorithms (MOEAs) – each implementing a different search paradigm – by comparing run-time converge... Read More about Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems..

WEC: weighted ensemble of text classifiers. (2020)
Conference Proceeding
UPADHYAY, A., NGUYEN, T.T., MASSIE, S. and MCCALL, J. 2020. WEC: weighted ensemble of text classifiers. In Proceedings of 2020 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2020), part of the 2020 (IEEE) World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 International joint conference on neural networks (IJCNN 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, Glasgow, UK [virtual conference]. Piscataway: IEEE [online], article ID 9185641. Available from: https://doi.org/10.1109/CEC48606.2020.9185641

Text classification is one of the most important tasks in the field of Natural Language Processing. There are many approaches that focus on two main aspects: generating an effective representation; and selecting and refining algorithms to build the c... Read More about WEC: weighted ensemble of text classifiers..

Racing strategy for the dynamic-customer location-allocation problem. (2020)
Conference Proceeding
ANKRAH, R., LACROIX, B., MCCALL, J., HARDWICK, A., CONWAY, A. and OWUSU, G. 2020. Racing strategy for the dynamic-customer location-allocation problem. In Proceedings of 2020 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2020), part of the 2020 (IEEE) World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 International joint conference on neural networks (IJCNN 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, Glasgow, UK [virtual conference]. Piscataway: IEEE [online], article 9185918. Available from: https://doi.org/10.1109/CEC48606.2020.9185918

In previous work, we proposed and studied a new dynamic formulation of the Location-allocation (LA) problem called the Dynamic-Customer Location-allocation (DC-LA) prob­lem. DC-LA is based on the idea of changes in customer distribution over a define... Read More about Racing strategy for the dynamic-customer location-allocation problem..

Evolved ensemble of detectors for gross error detection. (2020)
Conference Proceeding
NGUYEN, T.T., MCCALL, J., WILSON, A., OCHEI, L., CORBETT, H. and STOCKTON, P. 2020. Evolved ensemble of detectors for gross error detection. In GECCO '20: proceedings of the Genetic and evolutionary computation conference companion (GECCO 2020), 8-12 July 2020, Cancún, Mexico. New York: ACM [online], pages 281-282. Available from: https://doi.org/10.1145/3377929.3389906

In this study, we evolve an ensemble of detectors to check the presence of gross systematic errors on measurement data. We use the Fisher method to combine the output of different detectors and then test the hypothesis about the presence of gross err... Read More about Evolved ensemble of detectors for gross error detection..

Multi-layer heterogeneous ensemble with classifier and feature selection. (2020)
Conference Proceeding
NGUYEN, T.T., VAN PHAM, N., DANG, M.T., LUONG, A.V., MCCALL, J. and LIEW, A.W.C. 2020. Multi-layer heterogeneous ensemble with classifier and feature selection. In GECCO '20: proceedings of the Genetic and evolutionary computation conference (GECCO 2020), 8-12 July 2020, Cancun, Mexico. New York: ACM [online], pages 725-733. Available from: https://doi.org/10.1145/3377930.3389832

Deep Neural Networks have achieved many successes when applying to visual, text, and speech information in various domains. The crucial reasons behind these successes are the multi-layer architecture and the in-model feature transformation of deep le... Read More about Multi-layer heterogeneous ensemble with classifier and feature selection..

Confidence in prediction: an approach for dynamic weighted ensemble. (2020)
Conference Proceeding
DO D.T., NGUYEN T.T., NGUYEN T.T., LUONG A.V., LIEW A.W.-C. and MCCALL J. 2020. Confidence in prediction: an approach for dynamic weighted ensemble. In Nguyen N., Jearanaitanakij K., Selamat A., Trawiński B. and Chittayasothorn S. (eds.) Intelligent information and database systems: proceedings of the 12th Asian intelligent information and database systems conference (ACIIDS 2020), 23-26 March 2020, Phuket, Thailand. Lecture Notes in Computer Science, 12033. Cham: Springer [online], part 1, pages 358-370. Available from: https://doi.org/10.1007/978-3-030-41964-6_31

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this... Read More about Confidence in prediction: an approach for dynamic weighted ensemble..

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..

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..

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..

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..

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..

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..

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..

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..

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..

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..