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Doocy, L., Prager, S. D., Kider, J. T., Jr., & Wiegand, R. P. (2019). Robust path matching and anomalous route detection using posterior weighted graphs. ACM Transactions on Spatial Algorithms and Systems, 5(2), 1-19.

Hanes, J., & Wiegand, R. P. (2019). Analytical and evolutionary methods for finding cut volumes in fault trees constrained by location. IEEE Transactions on reliability, 68(4), 1214-1226.

Bari, A. T. M. G., Gaspar, A., Wiegand, R. P., Albert, J. L., Bucci, A., & Kumar, A. N. (2019). EvoParsons: design implementation and preliminary evaluation of evolutionary Parsons puzzle. Genetic Programming and Evolvable Machines, 20(2), 213-244.

Bari, A. G., Gaspar, A., Wiegand, R. P., & Bucci, A. (2018, July). Selection methods to relax strict acceptance condition in test-based coevolution. 2018 IEEE Congress on Evolutionary Computation (CEC).

Fandango, A., & Wiegand, R. P. (2018). Towards investigation of interactive strategy for data mining of short-term traffic flow with Recurrent Neural Networks. Proceedings of the 2nd International Conference on Information System and Data Mining – ICISDM’18.

Giroux, A. L., Harper, C., & Wiegand, R. P. (2017). Evaluating multi-criteria connection mechanisms: A new algorithm for browsing digital archives. Digital Scholarship in the Humanities, 33(3), 540-547.

Gaspar, A., Bari, A. T. M. G., Kumar, A. N., Bucci, A., Wiegand, R. P., & Albert, J. L. (2016, November). Evolutionary practice problems generation: Design guidelines. 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI).

Hanes, J., & Wiegand, R. P. (2015). Using L-systems to generate fault treses for benchmarking and testing. Proceedings of the 28th International Florida Artificial Intelligence Symposium, 173–178.

Wu, A. S., Wiegand, R. P., & Pradham, R. (2015). Building redundancy in multi-agent systems using probabilistic action. Proceedings of the 28th International Florida Artificial Intelligence Symposium.

Mondesire, S., & Wiegand, R. P. (2014). Forgetting beneficial knowledge in decomposition-based reinforcement learning using evolutionary computation. Proceedings of the International Conference on Genetic and Evolutionary Methods (GEM).