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Evolutionary Computation

Evolutionary computation is a family of algorithms for global optimization inspired by biological evolution

Copyrights and Credit for: brokenmachine86

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AP Photo/Martin Meissner

Artificial Intelligence

The attempt to create human-level intelligence processes in computing machines

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Genetic Improvement

Genetic Improvement is a subfield of Evolutionary Computation which starts not from randomly created initial individuals, but from functional individuals that are subsequently improved through variation and selection

Picture courtesy: Here

About Us

Based at Michigan State University, we work on various projects related to applying evolutionary algorithms in software and the real world. We also study more fundamental questions in Genetic Programming and other EC methods around regulation, open-ended evolution, novelty and search space dynamics. Algorithms influenced by ecology and social systems are another topic of interest, so is the field of Artificial Life in general and its subfield of Artificial Chemistries.

Evolutionary Computation

Evolutionary Computation is a field of optimization theory where instead of using classical numerical methods to solve optimization problems, we use inspiration from biological evolution to 'evolve' good solutions.

Artificial Intelligence

Artificial intelligence is the attempt to create human-level intelligence processes in computing machines. Specific applications of AI include pattern recognition, e.g. speech and image recognition as well as decision support. Recently artificial neural networks (ANNs) have become very prominent in AI with deep learning processes.

Genetic Improvement

Genetic Improvement is a subfield of Evolutionary Computation which starts not from randomly created initial individuals, but from functional individuals that are subsequently improved through variation and selection.

Upcoming Conference: EuroGP 2025

From the conference website: "EuroGP is the premier annual conference on Genetic Programming (GP), the oldest and the only meeting worldwide devoted specifically to this branch of evolutionary computation. It is always a high-quality, enjoyable, friendly event, attracting participants from all continents, and offering excellent opportunities for networking, informal contact, and exchange of ideas with fellow researchers. It will feature a mixture of oral presentations and poster sessions and invited keynote speakers."

Submission Deadline: 1 November 2024
Evo2025 Conference Flyer

Research Highlights

Here are some of our selected research projects

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POET

Developing peptides for therapeutic targets or biomarkers is a challenging task within the realm of protein engineering. Evolutionary algorithms are a promising approach for addressing this task. In this context, we have developed POET, a tool based on genetic programming to evolve sequence-function models. These models are then used to predict peptides for experimental evaluation. A POET extension has been introduced using regular expressions. Combining regular expressions with genetic programming provides a promising research direction for the discovery of new efficient peptides.

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Genotype-Phenotype Mapping

Mapping between the genotype and phenotype of Tree Genetic Programming is an open field of study, as the genotype may contain sub-structures that contribute nothing to the phenotype. We study this phenomenon and have created a method to remove ineffective nodes from the trees.

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Active Learning

The diversity in genetic programming model populations can be exploited to select training samples that maximally inform the development of models through evolution.

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General-Purpose Optimization Library

In development by I. Bakurov, the GPOL aims to include a wide range of optimization methods, including traditional search and genetic algorithms.

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BeefMesh

A secure and scalable digital leder design to support data centric applications with reliable traceability information sharing across supply chain networks.

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GE and LLMs

Grammatical Evolution aims to evolve programs based on defined grammars given to the tool. A. Murphy works on using GE to improve Large-Language Model performance.

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Spatial Genetic Programming

Spatial Genetic Programming considers space as a first-order effect to optimize which aids with determining the suitable order of execution of LGP programs to solve given problems and causes spatial dynamics to appear in the system.

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Cartesian GP Crossover

Researchers have struggled to develop beneficial crossover operators in Cartesian GP despite the success of crossover in the similar Linear GP algorithm. M. Kocherovsky works to find out why this happens.

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Sharpness in GP

I. Bakurov and N. Haut have been integrating a measure of model stability called "Sharpness" into GP algorithms. A program must not only be accurate, but it should be robust to preturbations in the data.

Lab Members

Wolfgang Banzhaf Professor

Wolfgang Banzhaf is the John R. Koza Chair for Genetic Programming in the Department of Computer Science and Engineering. His research interests are in the field of bio-inspired computing, notably evolutionary computation and complex adaptive systems.

Email: banzhafw AT msu.edu

Nathan Haut (he / him / his) Professor - Fixed Term

My research is focused on developing active learning methods for genetic programming systems to reduce training data requirements for tasks such as symbolic regression, classification, and image segmentation.

Email: hautnath AT msu.edu

Illya Bakurov Postdoc

I obtained my Ph.D. at NOVA Information Management (NOVA-IMS) School in Lisbon, under the supervision of Professors Leonardo Vanneschi, Mauro Castelli and Raimondo Schettini. My research concerns Evolutionary Computation and its application in the fields of Image Processing and Computer Vision. During my studies, I spent two years working in the Imaging and Vision Laboratory, directed by Prof. Raimondo Schettini, at Università degli Studi di Milano Bicocca (UNIMIB). After getting my Ph.D., I was employed as Auxiliary Invited Professor at NOVA-IMS, where I lectured, supervised students and conducted my research. Now it's time for a new journey at the Banzhaf Lab!

Aidan Murphy Postdoc

I am currently working on the POET project within the Banzhaf lab, investigating different techniques to improve automatic peptide design. I received my bachelor's degree in theoretical physics from Trinity College Dublin and Ph.D. degree in explainable Artificial Intelligence (AI) (XAI) from the BDS Laboratory, University of Limerick. I was previously a Postdoctoral Research Fellow within the Complex Software Laboratory, University College Dublin, researching software testing and mutation analysis and am currently an assistant professor in UCD. My research interests include grammatical evolution, transfer learning, fuzzy logic, genetic improvement and XAI.

Marzieh Kianinejad PhD Student

I joined the Banzhaf Lab on January 1st of 2024. I am interested in evolutionary computation, mathematics, and machine learning. I’m working on Linear Genetic programming and Cartesian Genetic programming.

Email: kianinej AT msu.edu

Mark Kocherovsky PhD Student

My research focus is on Cartesian Genetic Programming and Linear Genetic Programming, specifically why crossover operators hinder search in CGP but not LGP. I also work on the POET project to develop a general tool for protein screening before synthesis. I enjoy photography, ice cream, hugging my cat, and reading!

Photo Credit: Benjamin Hatto

Email: kocherov AT msu.edu

Ruchika Gupta PHD Student

I am Ruchika Gupta. I am currently interested in image processing and how genetic programming can play a role in current image processing techniques. I am also interested in the subdomain of LLM's and how genetic programming can help play a role in this.

Email: guptaru1 AT msu.edu

Zachary Perrico PhD Student

After earning my B.S. in Computer Science and Pure Mathematics, I joined the Banzhaf Lab where I am currently a first-year Ph.D. student. My interests include the geometry of Euclidian and non-Euclidian n-space, cellular automata, and alternate models of computation. I aim to integrate these concepts into evolutionary computation to enhance and generalize existing models.

Email: perricoz AT msu.edu

Tri Khuc Undergrad

I am a junior undergrad studying Computer Science with a minor in Actuarial Science. My research interests include machine/deep learning and computational finance.

Email: khuctri AT msu.edu

Selected Publications

2024

  1. Bakurov, I. Haut, N., Banzhaf, W. Sharpness-Aware Minimization in Genetic Programming. Genetic Programming Theory and Practice XXI, June 06-08, 2024.
  2. Banzhaf, W., Bakurov, I. On the Nature of the Phenotype in Tree Genetic Programming. Conference: Genetic and Evolutionary Computation Conference (GECCO), July 2024.
  3. F. Marchetti, M. Castelli, I. Bakurov, L. Vanneschi. Full Inclusive Genetic Programming. IEEE Congress on Evolutionary Computation (CEC) 2024, 30 Jun - 5 Jul 2024.
  4. Bakurov, I., Muñoz Contreras, J.M., Castelli, M. et al. Geometric semantic genetic programming with normalized and standardized random programs. Genetic Programming & Evolvable Machines 25, 6 (2024). https://doi.org/10.1007/s10710-024-09479-1

2023

  1. Zhang, Hengzhe, Qi Chen, Bing Xue, Wolfgang Banzhaf, and Mengjie Zhang. 2023b. “Modular Multi-Tree Genetic Programming for Evolutionary Feature Construction for Regression.” IEEE Transactions on Evolutionary Computation.
  2. Trujillo, Leonardo, Stephan M Winkler, Sara Silva, and Wolfgang Banzhaf. 2023. “Genetic Programming Theory and Practice XIX.” Springer Nature.
  3. Yuan, Yuan, and Wolfgang Banzhaf. 2023. “Iterative Genetic Improvement: Scaling Stochastic Program Synthesis.” Artificial Intelligence, 103962.
  4. Zhang, Hengzhe, Qi Chen, Alberto Tonda, Bing Xue, Wolfgang Banzhaf, and Mengjie Zhang. 2023. “MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning.” In European Conference on Genetic Programming (Part of EvoStar), 84–100. Springer Nature Switzerland Cham.
  5. Zhang, Hengzhe, Qi Chen, Bing Xue, Wolfgang Banzhaf, and Mengjie Zhang. 2023a. “A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for Regression.” In Proceedings of the Genetic and Evolutionary Computation Conference, 1194–1202.
  6. Banzhaf, Wolfgang, and Mengjie Zhang. 2023. “MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning.” In Genetic Programming: 26th European Conference, EuroGP 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings, 13986:84. Springer Nature.
  7. Bao, Honglin, Zachary P Neal, and Wolfgang Banzhaf. 2023. “Coevolutionary Opinion Dynamics with Sparse Interactions in Open-Ended Societies.” Complex & Intelligent Systems 9 (1): 565–77.
  8. Bakurov, I., Buzzelli, M., Schettini, R. et al. Semantic Segmentation Network Stacking with Genetic Programming. Genetic Programming & Evolvable Machines 24, 15 (2023). https://doi.org/10.1007/s10710-023-09464-0
  9. Chen, Qi, Bing Xue, Wolfgang Banzhaf, and Mengjie Zhang. 2023. “Relieving Genetic Programming from Coefficient Learning for Symbolic Regression via Correlation and Linear Scaling.” In Proceedings of the Genetic and Evolutionary Computation Conference, 420–28.
  10. Guha, Ritam, Wei Ao, Stephen Kelly, Vishnu Boddeti, Erik Goodman, Wolfgang Banzhaf, and Kalyanmoy Deb. 2023. “MOAZ: A Multi-Objective AutoML-Zero Framework.” In Proceedings of the Genetic and Evolutionary Computation Conference, 485–92.
  11. Haut, Nathan. Active Learning in Genetic Programming. PhD thesis, 2023. ProQuest LLC
  12. Haut, Nathan, Banzhaf, Wolfgang, Punch, Bill, and Colbry, Dirk. Accelerating Image Analysis Research with Active Learning Techniques in Genetic Programming. Springer Nature Singapore, Singapore, 2024, pp. 45–64
  13. Haut, Nathan, Bill Punch, and Wolfgang Banzhaf. 2023. “Active Learning Informs Symbolic Regression Model Development in Genetic Programming.” In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 587–90.
  14. Hu, Ting, Gabriela Ochoa, and Wolfgang Banzhaf. 2023. “Phenotype Search Trajectory Networks for Linear Genetic Programming.” In European Conference on Genetic Programming (Part of EvoStar), 52–67. Springer Nature Switzerland Cham.
  15. Kelly, Stephen, Daniel S Park, Xingyou Song, Mitchell McIntire, Pranav Nashikkar, Ritam Guha, Wolfgang Banzhaf, et al. 2023. “Discovering Adaptable Symbolic Algorithms from Scratch.” arXiv Preprint arXiv:2307.16890.
  16. Scalzitti, N., Miralavy, I., Korenchan, D. E., Farrar, C. T., Gilad, A. A., & Banzhaf, W. (2023). Computational peptide discovery with a genetic programming approach. Pre-print in Research Square
  17. Miralavy, I., & Banzhaf, W. (2023). Spatial Genetic Programming. In European Conference on Genetic Programming (Part of EvoStar) (pp. 260-275). Cham: Springer Nature Switzerland.
  18. Bricco, A. R., Miralavy, I., Bo, S., Perlman, O., Korenchan, D. E., Farrar, C. T., ... & Gilad, A. A. (2023). A Genetic Programming Approach to Engineering MRI Reporter Genes. ACS Synthetic Biology, 12(4), 1154-1163.
  19. Nathan Haut, Bill Punch, and Wolfgang Banzhaf. (2023). Active Learning Informs Symbolic Regression Model Development in Genetic Programming. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO '23 Companion). Association for Computing Machinery, New York, NY, USA, 587–590. https://doi.org/10.1145/3583133.3590577
  20. Haut, N., Banzhaf, W., Punch, B. (2023). Correlation Versus RMSE Loss Functions in Symbolic Regression Tasks. In: Trujillo, L., Winkler, S.M., Silva, S., Banzhaf, W. (eds) Genetic Programming Theory and Practice XIX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-19-8460-0_2
  21. Haut, N., Banzhaf, W., & Punch, B. (2023). Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression. arXiv preprint arXiv:2308.00672.
  22. Bao, H., Neal, Z. P., & Banzhaf, W. (2022). Coevolutionary opinion dynamics with sparse interactions in open-ended societies. Complex & Intelligent Systems, 36. Online First: https://doi.org/10.1007/s40747-022-00810-w

2022

  1. Langdon, William B, and Wolfgang Banzhaf. 2022. “Long-Term Evolution Experiment with Genetic Programming [Hot of the Press].” In Proceedings of the Genetic and Evolutionary Computation Conference Companion, 29–30.
  2. Banzhaf, W., Trujillo, L., Winkler, S., & Worzel, B. (2022). Genetic Programming Theory and Practice XVIII. Springer Nature.
  3. Bricco, A. R., Miralavy, I., Bo, S., Perlman, O., Farrar, C. T., McMahon, M. T., ... & Gilad, A. A. (2022). Protein Optimization Evolving Tool (POET) based on Genetic Programming. bioRxiv.
  4. Haut, N., Banzhaf, W., & Punch, B. (2022). Active Learning Improves Performance on Symbolic RegressionTasks in StackGP. arXiv preprint arXiv:2202.04708.
  5. Li, S. S., Peeler, H., Sloss, A. N., Reid, K. N., & Banzhaf, W. (2022). Genetic Improvement in the Shackleton Framework for Optimizing LLVM Pass Sequences. arXiv preprint arXiv:2204.13261.
  6. Miralavy, I., & Banzhaf, W. (2022). An Artificial Chemistry Implementation of a Gene Regulatory Network. arXiv preprint arXiv:2209.04114.
  7. Miralavy, I., Bricco, A. R., Gilad, A. A., & Banzhaf, W. (2022). Using genetic programming to predict and optimize protein function. PeerJ Physical Chemistry, 4, e24.
  8. Miralavy, I., Bricco, A., Gilad, A., & Banzhaf, W. (2022). Using Genetic Programming to Predict and Optimize Protein Function. arXiv preprint arXiv:2202.04039.
  9. Peeler, H., Li, S. S., Sloss, A. N., Reid, K. N., Yuan, Y., & Banzhaf, W. (2022). Optimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework. arXiv preprint arXiv:2201.13305.
  10. Recio, G., Banzhaf, W., & White, R. (2022). From dynamics to novelty: An agent-based model of the economic system. Artificial Life, 28(1), 58-95.
  11. Yuan, Y., & Banzhaf, W. (2021). Expensive Multiobjective Evolutionary Optimization Assisted by Dominance Prediction. IEEE Transactions on Evolutionary Computation, 26(1), 159-173.

2021

  1. Rolla, J, A Machtay, A Patton, W Banzhaf, A Connolly, R Debolt, L Deer, et al. 2021. “Using Evolutionary Algorithms to Design Antennas with Greater Sensitivity to Ultra High Energy Neutrinos.” arXiv Preprint arXiv:2112.03246.
  2. Telikani, Akbar, Amirhessam Tahmassebi, Wolfgang Banzhaf, and Amir H Gandomi. 2021. “Evolutionary Machine Learning: A Survey.” ACM Computing Surveys (CSUR) 54 (8): 1–35.
  3. Banzhaf, Wolfgang. (2021). The effects of taxes on wealth inequality in Artificial Chemistry models of economic activity. PLoS One, 16(8), e0255719.
  4. Han, J., Gondro, C., Reid, K., & Steibel, J. P. (2021). Heuristic hyperparameter optimization of deep learning models for genomic prediction. G3, 11(7), jkab032.
  5. Kelly, S., Smith, R. J., Heywood, M. I., & Banzhaf, W. (2021). Emergent tangled program graphs in partially observable recursive forecasting and ViZDoom navigation tasks. ACM Transactions on Evolutionary Learning and Optimization, 1(3), 1–41.
  6. Kelly, S., Voegerl, T., Banzhaf, W., & Gondro, C. (2021). Evolving Hierarchical Memory-Prediction Machines in Multi-Task Reinforcement Learning. Genetic Programming and Evolvable Machines, 22, 573–605.
  7. Lu, Z., Whalen, I., Dhebar, Y., Deb, K., Goodman, E., Banzhaf, W., & Boddeti, V. N. (2021). Multi-Objective Evolutionary Design of Deep Convolutional Neural Net- works for Image Classification. IEEE Transactions on Evolutionary Computation, 25, 277–291.
  8. Lu, Zhichao, Sreekumar, G., Goodman, E., Banzhaf, W., Deb, K., & Boddeti, V. N. (2021). Neural architecture transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 2171–2189.
  9. Reid, K. N., Miralavy, I., Kelly, S., Banzhaf, W., & Gondro, C. (2021). The Factory Must Grow: Automation in Factorio. arXiv preprint arXiv:2102. 04871.
  10. Telikani, A., Tahmassebi, A. H., Banzhaf, W., & Gandomi, A. (2021). Evolutionary Machine Learning: A Survey. ACM Computing Surveys.
  11. Using Evolutionary Algorithms to Design Antennas with Greater Sensitivity to Ultra High Energy Neutrinos. (2021). arXiv:2112. 03246.
  12. Yuan, Yuan, & Banzhaf, W. (2021). Expensive Multi-Objective Evolutionary Optimization Assisted by Dominance Prediction. IEEE Transactions on Evolutionary Computation, EarlyAccess. doi:DOI 10.1109/TEVC.2021.3098257

2020

  1. Banzhaf, W. (2020). The Effects of Taxes on Wealth Inequality in Artificial Chemistry Models of Economic Activity. arXiv preprint arXiv:2007. 02934.
  2. Banzhaf, W., Cheng, B. H. C., Deb, K., Holekamp, K. E., Lenski, R. E., Ofria, C., … Whittaker, D. J. (Reds). (2020). Evolution in Action - Past, Present and Future: A Festschrift for Erik Goodman. Springer, Cham, Switzerland.
  3. Banzhaf, W., Goodman, E., Sheneman, L., Trujillo, L., & Worzel, B. (Reds). (2020). Genetic Programming Theory and Practice XVII. Springer, Cham, Switzerland.
  4. Fernandez de Vega, F., Olague, G., Chavez, F., Lanza, D., Banzhaf, W., & Goodman, E. (2020). It is Time for New Perspectives on How to Fight Bloat in GP. arXiv preprint arXiv:2005. 00603.
  5. Hu, Ting, Tomassini, M., & Banzhaf, W. (2020). A network perspective on genotype-phenotype mapping in genetic programming. Genetic Programming and Evolvable Machines, 21(3), 375–397.
  6. Kelly, S., & Banzhaf, W. (2020). Temporal memory sharing in visual reinforcement learning. In W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, & B. Worzel (Reds), Genetic Programming Theory and Practice XVII (bll 101–119). Springer, Cham.
  7. Kelly, S., Newsted, J., Banzhaf, W., & Gondro, C. (2020). A modular memory framework for time series prediction. Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 949–957.
  8. Lu, Zhichao, Deb, K., Goodman, E., Banzhaf, W., & Boddeti, V. N. (2020). NSGANetV2: Evolutionary multi-objective surrogate-assisted neural architecture search. European Conference on Computer Vision ECCV-2020, LNCS, 12346, 35–51.
  9. Lu, Zhichao, Sreekumar, G., Goodman, E., Banzhaf, W., Deb, K., & Boddeti, V. N. (2020). Neural Architecture Transfer. arXiv preprint arXiv:2005. 05859.
  10. Lu, Zhichao, Whalen, I., Dhebar, Y., Deb, K., Goodman, E., Banzhaf, W., & Boddeti, V. N. (2020). Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification. IEEE Transactions on Evolutionary Computation, Early Access, DOI: 10.1109/TEVC.2020.3024708. doi:10.1109/TEVC.2020.3024708
  11. Recio, G., Banzhaf, W., & White, R. (2020). A Study of Severe Disruption in an Artificial Economy. Artificial Life Conference Proceedings, 180–187. MIT Press, Cambridge, MA.
  12. Whalen, I., Gondro, C., & Banzhaf, W. (2020). Evolving SNP Panels for Genomic Prediction. In W. Banzhaf, B. H. C. Cheng, K. Deb, K. E. Holekamp, R. E. Lenski, C. Ofria, … D. J. Whittaker (Reds), Evolution in Action - Past, Present and Future: A Festschrift for Erik Goodman (bll 467–487). Springer, Cham, Switzerland.
  13. White, D. R., Fowler, B., Banzhaf, W., & Barr, E. T. (2020). Modelling Genetic Programming as a Simple Sampling Algorithm. In W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, & B. Worzel (Reds), Genetic Programming Theory and Practice XVII (bll 367–381). Springer, Cham.
  14. White, R., & Banzhaf, W. (2020). Putting Natural Time into Science. In Adamatzky, A. and Kendon, & V. (Reds), From Astrophysics to Unconventional Computation (bll 1–21). Springer, Cham.
  15. Worzel, B., Trujillo, L., Sheneman, L., Goodman, E., & Banzhaf, W. (2020). Genetic Programming Theory and Practice XVII. Springer.
  16. Yuan, Y., & Banzhaf, W. (2020). Making Better Use of Repair Templates in Automated Program Repair: A Multi-Objective Approach. In W. Banzhaf, B. H. C. Cheng, K. Deb, K. E. Holekamp, R. E. Lenski, C. Ofria, … D. J. Whittaker (Reds), Evolution in Action - Past, Present and Future: A Festschrift for ErikGoodman (bll 485–507). Springer, Cham, Switzerland.
  17. Yuan, Yuan, & Banzhaf, W. (2020a). An Evolutionary System for Better Automatic Software Repair. In W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, & B. Worzel (Reds), Genetic Programming Theory and Practice XVII (bll 383–406). Springer, Cham.
  18. Yuan, Yuan, & Banzhaf, W. (2020b). ARJA: Automated repair of JAVA programs via multi-objective genetic programming. IEEE Transactions on Software Engineering, 46(10), 1040–1067.
  19. Yuan, Yuan, & Banzhaf, W. (2020c). Making better use of repair templates in automated program repair: A multi-objective approach. In Wolfgang Banzhaf, B. H. C. Cheng, K. Deb, K. E. Holekamp, R. E. Lenski, C. Ofria, … D. J. Whittaker (Reds), Evolution in Action: Past, Present and Future (bll 385–407). Springer, Cham.
  20. Yuan, Yuan, & Banzhaf, W. (2020d). Toward better evolutionary program repair: An integrated approach. ACM Transactions on Software Engineering and Methodology (TOSEM), 29(1), 1–53.
  21. de Vega, F. F., Olague, G., Chavez, F., Lanza, D., Banzhaf, W., & Goodman, E. (2020). It is time for new perspectives on how to fight bloat in GP. In W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, & B. Worzel (Reds), Genetic Programming Theory and Practice XVII (bll 25–38). Springer, Cham.
  22. de Vega, F. F., Olague, G., Lanza, D., Banzhaf, W., Goodman, E., Menendez-Clavijo, J., … Others. (2020). Time and individual duration in genetic programming. IEEE Access, 8, 38692–38713.

2019

  1. Banzhaf, W., & Hu, T. (2019). Evolutionary Computation. In D. J. Futuyama (Red), Oxford Bibliographies Evolutionary Biology. OUP.
  2. Banzhaf, W., Spector, L., & Sheneman, L. (Reds). (2019). Genetic Programming Theory and Practice XVI (GPTP-XVI). Springer, Cham, Switzerland.
  3. Bolandi, H., Banzhaf, W., Lajnef, N., Barri, K., & Alavi, A. H. (2019a). An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach. Technologies, 7(2), 42. doi:10.3390/technologies7020042
  4. Bolandi, H., Banzhaf, W., Lajnef, N., Barri, K., & Alavi, A. H. (2019b). Bond strength prediction of FRP-bar reinforced concrete: a multi-gene genetic programming approach (bl 364). ACM Press.
  5. Chilaka, C., Carr, S., Shalaby, N., & Banzhaf, W. (2019). Prediction of normalized signal strength on DNA-sequencing microarrays by n-grams within a neural network model. Bioinformation, 15(6), 388–393.
  6. Hu, Ting, Tomassini, M., & Banzhaf, W. (2019). Complex Network Analysis of a Genetic Programming Phenotype Network. European Conference on Genetic Programming, 49–63. Springer, Cham.
  7. Langdon, W. B., & Banzhaf, W. (2019). Continuous long-term evolution of genetic programming. The 2019 Conference on Artificial Life: A Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALIFE), 388–395. MIT Press.
  8. Langdon, William B., & Banzhaf, W. (2019). Faster Genetic Programming GPquick via multicore and Advanced Vector Extensions. arXiv preprint arXiv:1902. 09215.
  9. Lu, Z., Whalen, I., Dhebar, Y., Deb, K., Goodman, E., Banzhaf, W., & Boddeti, V. N. (2019). Multi-Criterion Evolutionary Design of Deep Convolutional Neural Networks. arXiv preprint arXiv:1912. 01369.
  10. Lu, Zhichao, Whalen, I., Boddeti, V., Dhebar, Y., Deb, K., Goodman, E., & Banzhaf, W. (2019). NSGA-Net: neural architecture search using multi-objective genetic Algorithm. In A. Auger & T. Stuetzle (Reds), Proceedings of the Genetic and Evolutionary Computation Conference (bll 419–427). ACM Press.
  11. Melo, V. V., Vargas, D., & Banzhaf, W. (2019). Batch Tournament Selection for Genetic Programming. Proceedings of the Genetic and Evolutionary Computation - GECCO 2019, Prague, Czech Republic, 994–1002. ACM Press.
  12. Melo, Vinicius V., Vargas, D. V., & Banzhaf, W. (2019). Batch Tournament Selection for Genetic Programming. arXiv preprint arXiv:1904. 08658.
  13. White, R., & Banzhaf, W. (2019). Putting Natural Time into Science. arXiv preprint arXiv:1901.07357.
  14. Yuan, Yuan, & Banzhaf, W. (2019). A hybrid evolutionary system for automatic software repair. In A. Auger & T. Stuetzle (Reds), Proceedings of the Genetic and Evolutionary Computation Conference, 2019 (bll 1417–1425). ACM Press.

2018

  1. Banzhaf, W. (2018). Some Remarks on Code Evolution with Genetic Programming. In S. Stepney & A. Adamatzky (Reds), Inspired by Nature, Essays Presented to Julian F. Miller on the Occasion of his 60th Birthday (bll 145–156). Springer, Cham, Switzerland.
  2. Banzhaf, W., Olson, R. S., Tozier, W., & Riolo, R. (Reds). (2018). Genetic Programming Theory and Practice XV (GPTP-XV). Springer, Cham, Switzerland.
  3. Bao, H., Wuyun, Q., & Banzhaf, W. (2018). Evolution of Cooperation through Genetic Collective Learning and Imitation in Multiagent Societies. In T. Ikegami, N. Virgo, O. Witkowski, M. Oka, R. Suzuki, & H. Iizuka (Reds), Proceedings of ALIFE XVI, Tokyo, Japan (bll 436–443). MIT Press.
  4. Cussat-Blanc, S., Harrington, K., & Banzhaf, W. (2018). Artificial Gene Regulatory NetworksA Review. Artificial Life, 24(4), 296–328.
  5. Dolson, E., Banzhaf, W., & Ofria, C. (2018). Applying Ecological Principles to Genetic Programming. In W. Banzhaf, R. S. Olson, W. Tozier, & R. Riolo (Reds), Genetic Programming Theory and Practice XV (GPTP-XV) (bll 73–88). Springer, Cham, Switzerland.
  6. Hu, T., & Banzhaf, W. (2018). Neutrality, Robustness, and Evolvability in Genetic Programming. In R. Riolo, B. Worzel, B. Goldman, & B. Tozier (Reds), Genetic Programming in Theory and Practice XIV (GPTP-XIV) (bll 101–117). Springer, Cham, Switzerland.
  7. Lu, Zhichao, Whalen, I., Boddeti, V., Dhebar, Y., Deb, K., Goodman, E., & Banzhaf, W. (2018). NSGA-NET: a multi-objective genetic algorithm for neural architecture search. arXiv preprint arXiv:1810. 03522.
  8. Moreno, M., Banzhaf, W., & Ofria, C. (2018). Learning an evolvable genotype-phenotype mapping. In H. Aguirre, K. Takamada, & E. al. (Reds), Proceedings of the Genetic and Evolutionary Computation - GECCO 2018, Kyoto, Japan (bll 983–990). ACM Press.
  9. Yuan, Y., & Banzhaf, W. (2018). ARJA: Automated repair of java programs via multi-objective genetic programming. IEEE Transactions on Software Engineering.
  10. de Melo, V., & Banzhaf, W. (2018a). Automatic feature engineering for regression modelswith machine learning: An evolutionary computation and statistics hybrid. InformationSciences, 430–431, 287–313.
  11. de Melo, V., & Banzhaf, W. (2018b). Drone Squadron Optimization: a novel self-adaptive algorithm for global numerical optimization. Neural Computing and Applications, 30, 3117–3144. doi:10.1007/s00521-017-2881-3

2017

  1. Chilaka, C., Carr, S., Shalaby, N., & Banzhaf, W. (2017). Use of a neural network to predict normalized signal strengths from a DNA sequencing microarray. Bioinformation, 13, 313–317.
  2. Ricalde, E., & Banzhaf, W. (2017). Evolving Adaptive Traffic Signal Controllers for a Real Scenario Using Genetic Programming with an Epigenetic Mechanism. 16th IEEE International Conference on Machine Learning and Applications (ICMLA-2017), 897–902. IEEE Press.
  3. Tafavi, A., & Banzhaf, W. (2017). A hybrid genetic programming decision making systemfor RoboCup soccer simulation. Proceedings of the Genetic and Evolutionary Computation- GECCO 2017, Berlin, Germany, 1025–1032. ACM Press.
  4. de Melo, V. V., & Banzhaf, W. (2017). Drone Squadron Optimization: a Self-adaptive Algorithm for Global Numerical Optimization. arXiv preprint arXiv:1703. 04561.
  5. de Melo, V., & Banzhaf, W. (2017). Improving the prediction of material properties of concrete using Kaizen Programming with Simulated Annealing. Neurocomputing, 246, 25–44.

2016

  1. Anaraki, J. R., Samet, S., & Banzhaf, W. (2016). New Fuzzy-Rough Hybrid Merit to Feature Selection. In J. F. Peters & A. Skowron (Reds), Transactions on Rough Sets XX: Vol LNCS 10020 (bll 1–23). Springer.
  2. Banzhaf, W., Baumgaertner, B., Beslon, G., Doursat, R., Foster, J. A., McMullin, B., … White, R. (2016). Defining and Simulating Open-Ended Novelty: Requirements, Guidelines, and Challenges. Theory in Biosciences, 135, 131–161.
  3. Fowler, B., & Banzhaf, W. (2016). Modelling Evolvability in Genetic Programming. In M. I. Heywood, J. McDermott, M. Castelli, E. Costa, & K. Sim (Reds), Proceedings EuroGP 2016, Porto, Portugal, 2016 (bll 215–229). Springer.
  4. Hu, Ting, & Banzhaf, W. (2016). Quantitative Analysis of Evolvability Using Vertex Centralities in Phenotype Network. In T. Friedrich, F. Neumann, & E. al. (Reds), Proceedings of the Genetic and Evolutionary Computation Conference 2016 (bll 733–740). doi:10.1145/2908812.2908940
  5. Kazemi, F., Banzhaf, W., & Gong, M. (2016). Human recognition through walking styles by multiwavelet transform. Proc. 8th International Conference on Information and Knowledge Technology (IKT-2016), 47–53. IEEE Press.
  6. Ricalde, Esteban, & Banzhaf, W. (2016). A Genetic Programming Approach for the Traffic Signal Control Problem with Epigenetic Modifications. In M. I. Heywood, J. McDermott, M. Castelli, E. Costa, & K. Sim (Reds), Proceedings EuroGP 2016, Porto, Portugal, 2016 (bll 133–148). Springer.
  7. Taylor, T., Bedau, M., Channon, A., Ackley, D., Banzhaf, W., Beslon, G., … Wise, M. (2016). Open-Ended Evolution: Perspectives from the OEE Workshop in York. Artificial Life, 22, 408–423.
  8. Wang, Y., Qian, Y., Li, Y., Gong, M., & Banzhaf, W. (2016). Artificial Multi-Bee-Colony Algorithm for k-Nearest-Neighbor Fields Search. In T. Friedrich, F. Neumann, & E. al. (Reds), Proceedings of the Genetic and Evolutionary Computation Conference 2016 (bll 1037–1044). New York, NY, USA: ACM.
  9. de Melo, V. V., & Banzhaf, W. (2016). Improving Logistic Regression Classification of Credit Approval with Features Constructed by Kaizen Programming. Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 61–62. ACM Press.
  10. de Melo, Vinicius Veloso, & Banzhaf, W. (2016). Kaizen Programming for Feature Construction for Classification. In R. Riolo, W. P. Worzel, M. Kotanchek, & A. Kordon (Reds), Genetic Programming - Theory and Practice XIII (bll 39–57). Springer.

Lab Alumni

Stephen Kelly Postdoc

I focus on genetic programming in reinforcement learning tasks. I am particularly interested in how emergent forms of memory and hierarchy allow digital evolution to build programs in partially-observable and multi-task environments. In addition to general problem solving, my collaborative research-creation projects apply bio-inspired computing in art/science hybrids that focus on storytelling, activism, and public engagement.

Email: kellys27 AT msu.edu

Yuan Yuan Postdoc

Yuan Yuan was a Postdoctoral Fellow with the Department of Computer Science and Engineering and a member of the BEACON Center for the Study of Evolution in Action at Michigan State University, USA. His research interests include evolutionary computation, machine learning, and search-based software engineering.

Email: yyuan AT msu.edu

Jory Schossau Postdoc

I finished my Ph.D. at Michigan State University in “Evolution of Decision-Making Systems.” My broad focus is on understanding the cultivation of features that make up intelligent and robust life-like behavior at all levels of complex systems.

Email: jory AT msu.edu

Honglin Bao (he / him / his) Graduate Student

Honglin was a graduate student at the Banzhaf Lab focusing on the intersection of social simulation, artificial life, complex systems, and computational social science. More info about him can be found in his page: carsonhlbao.com

Email: baohongl AT msu.edu

Ken Reid (he / him) Postdoc

Ken's primary research focus was the overlap of evolutionary algorithms with machine learning. Additionally, he was working on genomic prediction in animals and humans using AI, formulating problems from the video game `Factorio' for research into real-world optimization problems, and in employee scheduling problems.

Email: reidken1 AT msu.edu

Nicolas Scalzitti Postdoc

My thesis subject was about bioinformatics and was focused on genome annotation using deep learning and genetic programming approaches. I had the opportunity to discover and exploit bio-inspired algorithms that fascinated me. I joined the Banzhaf lab on September 1st of 2022, and I am focusing on the development of a new approach based on evolutionary algorithms for the POET project. I enjoy the fusion of biology with computer science to solve problems.

Email: scalzit1 AT msu.edu

Iliya Miralavy (he/him/his) Graduate Student

I am CSE PhD student and a researcher at BEACON. I'm interested in bio-inspired algorithms and applying them on complex real life problems. I'm currently working on my thesis Spatial Genetic Programming, Automation in Factorio and POET: Protein Optimization Evolving Tool. I love coding and implementing my ideas specially on computer games!

Email: miralavy AT msu.edu

Jun Guo Graduate Student

I am a PhD student in Mechanical Engineering. I am interested in developing a systematic approach to generating constitutive models for engineering materials using Genetic Programming. Using our approach, we can develop a constitutive model with comparable fitness, but it is much simpler and physically reasonable.

Email: guojun2 AT msu.edu

Salman Ali Graduate Student

I focus on utilizing digital ledger technologies for developing secure, traceable and trust oriented data sharing applications for food supply chains. My area of research lies in the overlap of blockchains, machine learning and big data technologies. During my time here, I have been focussing on using decentralized ledger technologies to collect and process big data for beef supply chain.

Email: alisalm1 AT msu DOT edu

Tatiana Voegerl (she/her/hers) Professorial Assistant

I am a sophomore undergrad studying Computer Science and Information Science with a Spanish minor. During my time on this team, I have contributed to research on Multi-Task Reinforcement Learning and learned about various elements of evolutionary computation through other team members.

Email: voegerlt AT msu.edu

Aman Dhruva Thamminana Undergrad

I am an undergrad student majoring in Computer Science and Mathematics with a minor in Entrepreneurship and Innovation at Michigan State University. I am working on automatic code repair and evolutionary program synthesis. I also love spontaneously traveling, listening to overplayed music and consuming too much coffee.

Email: thammina AT msu.edu

Stella Li Undergraduate RA

I am an undergraduate student from Johns Hopkins University working as a research assistant in the Banzhaf Lab starting May 2021. I am working on the implementation and evaluation of the genetic programming framework Shackleton that optimizes the sequence of LLVM compiler passes. I was able to add new features to the Shackleton Framework and improve the optimization from 2% to 20%.

Email: sli136 AT jhu.edu

Alexa Wiker Undergrad

I am a freshman undergrad studying Computer Science with a minor in Computational Mathematics, Science, and Engineering. My research interests include machine learning and evolutionary computation.

Email: wikerale AT msu.edu

MSU – BEACON Center Biomedical and Physical Sciences Building 567 Wilson Road Room 1441 East Lansing, MI 48824

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