Research output per year
Research output per year
Search results
-
2023
Analysis of a Pairwise Dominance Coevolutionary Algorithm And DefendIt
Lehre, P. K., Fajardo, M. H., Toutouh, J., Hemberg, E. & O'Reilly, U-M., 12 Jul 2023, GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM), p. 1027-1035 9 p. (GECCO: Genetic and Evolutionary Computation Conference).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
-
How Fitness Aggregation Methods Affect the Performance of Competitive CoEAs on Bilinear Problems
Hevia Fajardo, M. & Lehre, P. K., 12 Jul 2023, GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM), p. 1593-1601 9 p. (GECCO: Genetic and Evolutionary Computation Conference).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile5 Downloads (Pure) -
Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation
Hevia Fajardo, M., Lehre, P. K. & Lin, S., 30 Aug 2023, FOGA '23: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. Association for Computing Machinery (ACM), p. 73–83 11 p. (FOGA: Foundations of Genetic Algorithms).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile63 Downloads (Pure) -
Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation
Hevia Fajardo, M., Lehre, P. K. & Lin, S., 24 Jul 2023, GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. Association for Computing Machinery (ACM), p. 819–822 4 p. (GECCO: Genetic and Evolutionary Computation Conference).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
-
2022
Hard problems are easier for success-based parameter control
Hevia Fajardo, M. & Sudholt, D., 8 Jul 2022, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference. Fieldsend, J. E. (ed.). New York: Association for Computing Machinery , p. 796–804 9 p. (GECCO: Genetic and Evolutionary Computation Conference).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
-
2021
Self-adjusting offspring population sizes outperform fixed parameters on the cliff function
Fajardo, M. A. H. & Sudholt, D., 6 Sept 2021, FOGA '21: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. New York: Association for Computing Machinery (ACM), 15 p. 5. (FOGA: Foundations of Genetic Algorithms).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
-
Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter
Fajardo, M. A. H. & Sudholt, D., 26 Jun 2021, GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference. Chicano, F. (ed.). New York: Association for Computing Machinery (ACM), p. 1151–1159 9 p. (Genetic and Evolutionary Computation Conference (GECCO)).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
-
2020
On the choice of the parameter control mechanism in the (1+ (λ, λ)) Genetic Algorithm
Fajardo, M. A. H. & Sudholt, D., 25 Jun 2020, Proceedings of the 2020 Genetic and Evolutionary Computation Conference.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
-
2019
An empirical evaluation of success-based parameter control mechanisms for evolutionary algorithms
Fajardo, M. A. H., 13 Jul 2019, Proceedings of the Genetic and Evolutionary Computation Conference.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution