By Dr Angus Kenny

Conference Papers

Kenny A; Ray T; Singh H, 2025, 'Multi-objective L-shaped Test Functions', in Proceedings of the Genetic and Evolutionary Computation Conference, ACM, pp. 22 - 29, presented at GECCO '25: Genetic and Evolutionary Computation Conference, http://dx.doi.org/10.1145/3712256.3726323

Kenny A; Ray T; Limmer S; Singh HK; Rodemann T; Olhofer M, 2024, 'Using Bayesian Optimization to Improve Hyperparameter Search in TPOT', in Gecco 2024 Proceedings of the 2024 Genetic and Evolutionary Computation Conference, pp. 340 - 348, http://dx.doi.org/10.1145/3638529.3654061

Kenny A; Ray T; Limmer S; Singh HK; Rodemann T; Olhofer M, 2023, 'Hybridizing TPOT with Bayesian Optimization', in Gecco 2023 Proceedings of the 2023 Genetic and Evolutionary Computation Conference, pp. 502 - 510, http://dx.doi.org/10.1145/3583131.3590364

Kenny A; Ernst AT; Li X; Sun Y, 2019, 'An improved merge search algorithm for the constrained pit problem in open-pit mining', in Gecco 2019 Proceedings of the 2019 Genetic and Evolutionary Computation Conference, pp. 294 - 302, http://dx.doi.org/10.1145/3321707.3321812

Kenny A; Li X; Ernst AT, 2018, 'A merge search algorithm and its application to the constrained pit problem in mining', in Gecco 2018 Proceedings of the 2018 Genetic and Evolutionary Computation Conference, pp. 316 - 323, http://dx.doi.org/10.1145/3205455.3205538

Kenny A; Li X; Ernst AT; Thiruvady D, 2017, 'Towards solving large-scale precedence constrained production scheduling problems in mining', in Gecco 2017 Proceedings of the 2017 Genetic and Evolutionary Computation Conference, pp. 1137 - 1144, http://dx.doi.org/10.1145/3071178.3071241

Kenny A; Li X, 2017, 'A study on pre-training deep neural networks using particle swarm optimisation', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 361 - 372, http://dx.doi.org/10.1007/978-3-319-68759-9_30

Kenny A; Li X; Qin AK; Ernst AT, 2016, 'A population-based local search technique with random descent and jump for the steiner tree problem in graphs', in Gecco 2016 Proceedings of the 2016 Genetic and Evolutionary Computation Conference, pp. 333 - 340, http://dx.doi.org/10.1145/2908812.2908860

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