Dr Mario Alejandro Hevia Fajardo

Dr Mario Alejandro Hevia Fajardo

School of Computer Science
Research Fellow in Theory of Co-Evolutionary Computation

Contact details

Address
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK
Mario is a Research Fellow at the University of Birmingham in the School of Computer Science in the group of Prof. Per Kristian Lehre. Before coming to Birmingham, he obtained a PhD degree at the University of Sheffield under supervision of Prof. Dirk Sudholt and Prof. Pietro Oliveto.

His main research interests are in theory of evolutionary computation and randomized search heuristics. Currently he is working on the analysis of competitive co-evolutionary algorithms.

Dr Mario Alejandro Hevia Fajardo - personal webpage

Qualifications

  • PhD Computer Science, University of Sheffield, UK. 2022
    Title: Runtime Analysis of Success-Based Parameter Control Mechanisms for Evolutionary Algorithms on Multimodal Problems
  • MSc Data Analytics, University of Sheffield, 2018
  • BSc Mechatronics Engineering, ITESM, 2014

Research

Theory of evolutionary computation, in particular evolutionary and co-evolutionary algorithms.

  • Analysis of co-evolutionary algorithms in adversarial optimisation.
  • Design and analysis of parameter control mechanisms for bio-inspired optimisation. 

Publications

Recent publications

Article

Hevia Fajardo, MA & Sudholt, D 2024, 'Self-adjusting Population Sizes for Non-elitist Evolutionary Algorithms: Why Success Rates Matter', Algorithmica, vol. 86, no. 2, pp. 526–565. 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

Chapter

Hevia Fajardo, M, Lehre, PK, Toutouh, J, Hemberg, E & O'Reilly, U-M 2024, Analysis of a Pairwise Dominance Coevolutionary Algorithm with Spatial Topology. in S Winkler, L Trujillo, C Ofria & T Hu (eds), Genetic Programming Theory and Practice XX. 1 edn, Genetic and Evolutionary Computation, Springer Singapore, pp. 19-44. https://doi.org/10.1007/978-981-99-8413-8_2

Conference contribution

Hevia Fajardo, M & Lehre, PK 2024, Ranking Diversity Benefits Coevolutionary Algorithms on an Intransitive Game. in M Affenzeller, SM Winkler, AV Kononova, H Trautmann, T Tušar, P Machado & T Bäck (eds), Parallel Problem Solving from Nature – PPSN XVIII: 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14–18, 2024, Proceedings, Part III. vol. 3, Lecture Notes in Computer Science, vol. 15150, Springer, 18th International Conference on Parallel Problem Solving From Nature PPSN 2024, Hagenberg, Austria, 14/09/24. https://doi.org/10.1007/978-3-031-70071-2_14

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

View all publications in research portal