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. An unsolved problem, it remains poorly understood why crossover
— the exchange of genetic information between parents — hinders rather than helps search in CGP.
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.
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
Selected Articles
For a mostly-full list, please check my Google Scholar
Page
Evolutionary Computation
- 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