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Evolutionary Computation

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

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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

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.

Research Highlights

Here are some of our selected research projects

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Social Inspiration in Computing

How does the computing realm derive inspiration from social sciences, e.g., game theory, social simulation, psychology, and social networks?

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Genomic Prediction with AI

Using evolutionary computation, machine learning and other AI methods to estimate causative SNPs for specific phenotypic traits in animals and humans.

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A Reliable and Scalable Digital Ledger to Support Traceability in Supply Chains.

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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Factorio Project

Factorio provides a simulation of complex and dynamic real-world optimization problems: our research involves defining these problems, solving them, and providing an interface into the game with external optimizers.

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Evolving Hierarchical Memory-Prediction Machines

We evolve computer programs that can learn many spatiotemporal patterns simultaneously using a dynamic hierarchy of models, and show how this results in highly general behaviours in multitask time series prediction and control problems.

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POET: Protein Optimization Engineering Tool

POET is a Genetic Programming tool alternative to Directed Evolution, that evolves sequence-function models that can predict protein traits from their sequence to be evaluated in lab environment.

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Guo Jun's Dissertation

Developing a systematic approach to generating desirable constitutive models from test data using Genetic Programming.

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

Ken Reid (he / him) Postdoc

Ken's primary research focus is on evolutionary algorithms for genomic prediction in animals and humans: predicting traits based on genetic makeup and relationships. He is also interested in machine learning, 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

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. Feb 7 I begin with the Banzhaf Lab on several new or ongoing projects. More information forthcoming!

Email: jory 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 technnologies to collect and process big data for beef supply chain.

Email: alisalm1 AT msu DOT edu

Nathan Haut (he / him / his) Graduate Student

I am interested in designing active learning tools to use with genetic programming systems to help drive the data collection and experimentation process to gather data that is most informative.

Email: hautnath 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 underpinning computational gene regulatory networks with artificial chemistry, developing a GP for Python code synthesis, 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

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

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

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

Select Publications

2022

  1. 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.
  2. 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.
  3. Miralavy, I., Bricco, A., Gilad, A., & Banzhaf, W. (2022). Using Genetic Programming to Predict and Optimize Protein Function. arXiv preprint arXiv:2202.04039.
  4. 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.

2021

  1. Banzhaf, Wolfgang. (2021). The effects of taxes on wealth inequality in Artificial Chemistry models of economic activity. PLoS One, 16(8), e0255719.
  2. Han, J., Gondro, C., Reid, K., & Steibel, J. P. (2021). Heuristic hyperparameter optimization of deep learning models for genomic prediction. G3, 11(7), jkab032.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. Reid, K. N., Miralavy, I., Kelly, S., Banzhaf, W., & Gondro, C. (2021). The Factory Must Grow: Automation in Factorio. arXiv preprint arXiv:2102. 04871.
  8. Telikani, A., Tahmassebi, A. H., Banzhaf, W., & Gandomi, A. (2021). Evolutionary Machine Learning: A Survey. ACM Computing Surveys.
  9. Using Evolutionary Algorithms to Design Antennas with Greater Sensitivity to Ultra High Energy Neutrinos. (2021). arXiv:2112. 03246.
  10. 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

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

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

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