Research

My research interests center on Evolutionary Computation. An ever-growing field of study, evolutionary computation aims to mimic biological evolution for parameter optimization. My main research focus is on crossover in Cartesian Genetic Programming. It is typically believed that crossover is a hinderance to search, although recent studies have found this to be a misconception. We still study crossover to try and find more efficient operators for the benefit of practitioners. My other experience also covers Autonomous Vehicles, STEM Education, Particle Physics, and Neuroevolution.

Note that pirating any of my content (such as on LibGen if it exists) is completely fine, and you have my permission as long as citations are given as normal.

    Selected Articles

    For a mostly-full list, please check my Google Scholar Page
    Evolutionary Computation
    • Kocherovsky, M., Cui, H., Bakurov, I., Heider, M., Kalkreuth, R., Banzhaf, W. (2026). New Perspectives on Cartesian Genetic Programming: A Survey. In: Manzoni, L., Cussat-Blanc, S., Chen, Q. (eds) Genetic Programming. EuroGP 2026. Lecture Notes in Computer Science, vol 16521. Springer, Cham. https://doi.org/10.1007/978-3-032-23005-8_6
    • Kocherovsky, M., Bakurov, I., Banzhaf, W. (2026). Node Preservation and Its Effect on Crossover in Cartesian Genetic Programming. In: Manzoni, L., Cussat-Blanc, S., Chen, Q. (eds) Genetic Programming. EuroGP 2026. Lecture Notes in Computer Science, vol 16521. Springer, Cham. https://doi.org/10.1007/978-3-032-23005-8_5
    • Kocherovsky, M., Kianinejad, M., Bakurov, I., Banzhaf, W. (2025). On the Effectiveness of Crossover Operators in Cartesian Genetic Programming. In: Xue, B., Manzoni, L., Bakurov, I. (eds) Genetic Programming. EuroGP 2025. Lecture Notes in Computer Science, vol 15609. Springer, Cham. https://doi.org/10.1007/978-3-031-89991-1_5
    • Mark Kocherovsky, Wolfgang Banzhaf; July 22–26, 2024. "Crossover Destructiveness in Cartesian versus Linear Genetic Programming." Proceedings of the ALIFE 2024: Proceedings of the 2024 Artificial Life Conference. ALIFE 2024: Proceedings of the 2024 Artificial Life Conference. Online. (pp. 20). ASME. https://doi.org/10.1162/isal_a_00735
    • Kocherovsky, Mark, and Chan-Jin Chung. "Using Evolutionary Algorithms to Optimize Hyperparameters for Keras DeepLearning Models to Solve the Two Intertwined Spiral Problem." (2022). Poster. http://qbx6.ltu.edu/chung/papers/42_Kocherovsky_Using.pdf
    Autonomous Robotics
    • Schulte, Joseph, et al. "Autonomous human-vehicle leader-follower control using deep-learning-driven gesture recognition." Vehicles 4.1 (2022): 243-258. https://www.mdpi.com/2624-8921/4/1/16
    STEM Education
    • Shamir, Mirit, Mark Kocherovsky, and Chan-Jin Chung. "A paradigm for teaching math and computer science concepts in k-12 learning environment by integrating coding, animation, dance, music and art." 2019 IEEE Integrated STEM Education Conference (ISEC). IEEE, 2019. https://www.robofest.net/2019/ISEC_19.pdf
    • Chung, Chan-Jin, and Mark Kocherovsky. "CS+PA 2: Learning computer science with physical activities and animation—A MathDance experiment." 2018 IEEE Integrated STEM Education Conference (ISEC). IEEE, 2018. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8340497
    Nuclear Physics
    • Cody, Mary, et al. "Complementary two-particle correlation observables for relativistic nuclear collisions." Physical Review C 107.1 (2023): 014909. https://arxiv.org/pdf/2110.04884.pdf
    Other
    • Machine learning-based development of Gadolinium binding peptides Nir A. Dayan, Makayla Long, Nicolas Scalzitti, Iliya Miralavy, Daniel Holmes, Mark Kocherovsky, Wolfgang Banzhaf, Assaf A. Gilad bioRxiv 2025.12.14.694215; doi: https://doi.org/10.64898/2025.12.14.694215 note: This article is a preprint and has not been certified by peer review.

    Book Chapters

  • Kocherovsky, M., DeRose, G., Paul, N., Pleune, M., Chung, CJ. (2023). Autonomous Vehicle Steering through Convolutional and Recurrent Deep Learning. In Autonomous Vehicles and systems: A technological and societal perspective (1st ed., Ser. River Publishers Series in Automation, Control and Robotics, pp. 83–112). essay, RIVER PUBLISHERS. Purchase Here | Chapter Only
  • Schulte, J., Kocherovsky, M., Dombecki, J., Paul, N., Pleune, M., Chung, CJ. (2023). 2D and 3D Pose Estimation for Gesture Recognition in Deeplearning-driven Human–vehicle Leader–follower Systems. In Autonomous Vehicles and systems: A technological and societal perspective (1st ed., Ser. River Publishers Series in Automation, Control and Robotics, pp. 113–142). essay, RIVER PUBLISHERS. Purchase Here | Chapter Only