By Dr Angus Kenny

Conference Papers

Kenny A; Ray T; Limmer S; Singh HK; Rodemann T; Olhofer M, 2024, 'Using Bayesian Optimization to Improve Hyperparameter Search in TPOT', in Proceedings of the Genetic and Evolutionary Computation Conference, ACM, presented at GECCO '24: Genetic and Evolutionary Computation Conference, 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/10.114S/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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