Recent publications
Article
Hevia Fajardo, M & Sudholt, D 2023, 'Self-adjusting Population Sizes for Non-elitist Evolutionary Algorithms: Why Success Rates Matter', Algorithmica. https://doi.org/10.1007/s00453-023-01153-9
Fajardo, MAH & Sudholt, D 2022, 'Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (λ, λ)) Genetic Algorithm', ACM Transactions on Evolutionary Learning and Optimization, vol. 2, no. 4, 13. https://doi.org/10.1145/3564755
Conference contribution
Lehre, PK, Fajardo, MH, Toutouh, J, Hemberg, E & O'Reilly, U-M 2023, Analysis of a Pairwise Dominance Coevolutionary Algorithm And DefendIt. in GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 1027-1035, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583131.3590411
Hevia Fajardo, M & Lehre, PK 2023, How Fitness Aggregation Methods Affect the Performance of Competitive CoEAs on Bilinear Problems. in GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 1593-1601, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583131.3590506
Hevia Fajardo, M, Lehre, PK & Lin, S 2023, Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation. in FOGA '23: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. FOGA: Foundations of Genetic Algorithms, Association for Computing Machinery (ACM), pp. 73–83, Foundations of Genetic Algorithms XVII, Potsdam, Germany, 30/08/23. https://doi.org/10.1145/3594805.3607132
Hevia Fajardo, M, Lehre, PK & Lin, S 2023, Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation. in GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery (ACM), pp. 819–822, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583133.3590701
Hevia Fajardo, M & Sudholt, D 2022, Hard problems are easier for success-based parameter control. in JE Fieldsend (ed.), GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO: Genetic and Evolutionary Computation Conference, Association for Computing Machinery , New York, pp. 796–804, GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, United States, 9/07/22. https://doi.org/10.1145/3512290.3528781
Fajardo, MAH & Sudholt, D 2021, Self-adjusting offspring population sizes outperform fixed parameters on the cliff function. in FOGA '21: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms., 5, FOGA: Foundations of Genetic Algorithms, Association for Computing Machinery (ACM), New York, FOGA '21: Foundations of Genetic Algorithms XVI, 6/09/21. https://doi.org/10.1145/3450218.3477306
Fajardo, MAH & Sudholt, D 2021, Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter. in F Chicano (ed.), GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference (GECCO), Association for Computing Machinery (ACM), New York, pp. 1151–1159, 2021 Genetic and Evolutionary Computation Conference, GECCO 2021, Virtual, Online, France, 10/07/21. https://doi.org/10.1145/3449639.3459338
Fajardo, MAH & Sudholt, D 2020, On the choice of the parameter control mechanism in the (1+ (λ, λ)) Genetic Algorithm. in Proceedings of the 2020 Genetic and Evolutionary Computation Conference. https://doi.org/10.1145/3377930.3390200
Fajardo, MAH 2019, An empirical evaluation of success-based parameter control mechanisms for evolutionary algorithms. in Proceedings of the Genetic and Evolutionary Computation Conference. https://doi.org/10.1145/3321707.3321858
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