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ORCIDMario Alejandro Hevia Fajardo
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2020 – today
- 2024
[j3]Mario Alejandro Hevia Fajardo
, Dirk Sudholt:
Self-adjusting offspring population sizes outperform fixed parameters on the cliff function. Artif. Intell. 328: 104061 (2024)- [j2]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Self-adjusting Population Sizes for Non-elitist Evolutionary Algorithms: Why Success Rates Matter. Algorithmica 86(2): 526-565 (2024) - [c12]Mario Alejandro Hevia Fajardo, Erik Hemberg, Jamal Toutouh, Una-May O'Reilly, Per Kristian Lehre:
A Self-adaptive Coevolutionary Algorithm. GECCO 2024 - [c11]Mario Alejandro Hevia Fajardo, Per Kristian Lehre:
Ranking Diversity Benefits Coevolutionary Algorithms on an Intransitive Game. PPSN (3) 2024: 213-229 - 2023
- [c10]Mario Alejandro Hevia Fajardo, Per Kristian Lehre, Shishen Lin:
Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation. FOGA 2023: 73-83 - [c9]Mario Alejandro Hevia Fajardo, Per Kristian Lehre, Shishen Lin:
Runtime Analysis of a Co-Evolutionary Algorithm: Overcoming Negative Drift in Maximin-Optimisation. GECCO Companion 2023: 819-822 - [c8]Per Kristian Lehre, Mario Alejandro Hevia Fajardo, Jamal Toutouh, Erik Hemberg, Una-May O'Reilly:
Analysis of a Pairwise Dominance Coevolutionary Algorithm And DefendIt. GECCO 2023: 1027-1035 - [c7]Mario Alejandro Hevia Fajardo, Per Kristian Lehre:
How Fitness Aggregation Methods Affect the Performance of Competitive CoEAs on Bilinear Problems. GECCO 2023: 1593-1601 - [c6]Mario Alejandro Hevia Fajardo, Per Kristian Lehre, Jamal Toutouh, Erik Hemberg, Una-May O'Reilly:
Analysis of a Pairwise Dominance Coevolutionary Algorithm with Spatial Topology. GPTP 2023: 19-44 - 2022
- [j1]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Theoretical and Empirical Analysis of Parameter Control Mechanisms in the (1 + (λ, λ)) Genetic Algorithm. ACM Trans. Evol. Learn. Optim. 2(4): 13:1-13:39 (2022) - [c5]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Hard problems are easier for success-based parameter control. GECCO 2022: 796-804 - [i2]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Hard Problems are Easier for Success-based Parameter Control. CoRR abs/2204.05817 (2022) - 2021
- [c4]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Self-adjusting offspring population sizes outperform fixed parameters on the cliff function. FOGA 2021: 5:1-5:15 - [c3]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Self-adjusting population sizes for non-elitist evolutionary algorithms: why success rates matter. GECCO 2021: 1151-1159 - [i1]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
Self-Adjusting Population Sizes for Non-Elitist Evolutionary Algorithms: Why Success Rates Matter. CoRR abs/2104.05624 (2021) - 2020
- [c2]Mario Alejandro Hevia Fajardo, Dirk Sudholt:
On the choice of the parameter control mechanism in the (1+(λ, λ)) genetic algorithm. GECCO 2020: 832-840
2010 – 2019
- 2019
- [c1]Mario Alejandro Hevia Fajardo:
An empirical evaluation of success-based parameter control mechanisms for evolutionary algorithms. GECCO 2019: 787-795
Coauthor Index
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