Evolutionary Computation and Machine Learning

• Evolving to find optimizations humans miss: Using evolutionary computation to improve GPU code for bioinformatics applications. J. Liou, M. Awan, P. Sulc, K. Leyba, S. Hofmeyr, C. Wu, and S. Forrest. ACM Transactions on Evolutionary Learning and Optimization (in revision).
• Automatically mitigating vulnerabilities in binary programs via partially recompilable decompilation. P. Reiter, H.J. Tay, W. Weimer, A. Doupe, R. Wang, and S. Forrest. ACM Trans. on Dependable and Secure Computing (in revision).
• Automated program repair. J. Renzullo, P. Reiter, W. Weimer, and S. Forrest Computing Surveys (in revision).
• Evolving Software: Combining Online Learning with Mutation-Based Stochastic Search J. Renzullo, W. Weimer, S. Forrest ACM Trans. on Evolutionary Learning and Optimization 3:4 (2023).
• Combining online learning with mutation-based stochastic search to repair buggy programs J. Renzullo, W. Weimer, and S. Forrest. Genetic and Evolutionary Computation Conference (GECCO), 2024 (hot off the press track, 4 page version of TELO paper).
• A multi-objective genetic algorithm for location in interaction testing R. Dougherty, D. Green, H. Kang, G. Kim, and S. Forrest. Genetic and Evolutionary Computations Conference (GECCO), 2024 (poster presentation).
• Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation A. Olsona, T. Rodgers, B. Donohoea, K. M. Pottera, S. A. Roberts, R. Pokharelc, S. Forrest, and N. W. Moorea. Computational Mechanics 72:525-551, 2023 (special issue on digital twins).
• Understanding the power of evolutionary computation for GPU code optimization J. Liou, M. Awan, S. Hofmeyr, C. Wu, and S. Forrest. IEEE Symposium on Workload Characterization, 2022.
• Digging into semantics: Where do search-based software repair methods search? H. Ahmad, P. Cashin, W. Weimer, and S. Forrest. /Seventeenth International Conference on Parallel Problem Solving from Nature (PPSN XVII), 2022.
• Improving source-code representations to enhance search-based software repair P. Reiter, A. Espinoza, R. Wang, A. Doupe, W. Weimer, and S. Forrest. Improving source-code representations to enhance search-based software repair. In Genetic and Evolutionary Computation Conference (GECCO'22), 2022.
• AI reflections in 2021 C. Buckner, R. Miikkulainen, S. Forrest, et al. Nature Machine Intelligence Feature 4:5-10, 2022.
• Confronting domain shift in trained neural networks C. Martinez, D. A. Najera-Flores, A. R. Brink, D. D. Quinn, E. Chatzi, and S. Forrest. Proc. of Machine Learning Research (PMLR) 148:176-192 (2021).
• A biological perspective on evolutionary computation R. Miikkulainen and S. Forrest Nature Machine Intelligence 3:1-7, 2021.
• MA-ABC: A Memetic Algorithm Optimizing Attractiveness, Balance, and Cost for Capacitated Arc Routing Problems M. Ramamoorthy, S. Forrest, and V. Syrotiuk. In Genetic and Evolutionary Computation Conf. (GECCO) (2021).
• Post-compiler performance tuning for general-purpose gpu kernels J. Liou, X. Wang, S. Forrest, and C. Wu. ACM Trans. on Architecture and Code Optimization 17:4, 2020.
• Automatically evolving a general controller for robot swarms J. Eriksen, M. Moses and S. Forrest, IEEE Symposium on Artificial Life, 2017.
• Adaptive computation: The multidisciplinary legacy of John H. Holland Communications of the ACM 59(8):58–63 (2016) doi 10.1145/2964342.
• Repairing COTS router firmware without access to source code or test suites: A case study in evolutionary software repair E. Schulte, W. Weimer, and S. Forrest The First International Genetic Improvement Workshop, 2015 (best paper award).
• Post-compiler software optimization for reducing energy E. Schulte, J. Dorn, S. Forrest, and W. Weimer, Nineteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS, 2014).
• Leveraging program equivalence for adaptive program repair: Models and first results W. Weimer, Z. Fry and S. Forrest Automated Softare Engineering (ASE) Conference (2013).
• Software mutational robustness Schulte, Z. P. Fry, E. Fast, W. Weimer and S. Forrest. Genetic Programming and Evolvable Machines 5(3):281–312 (2014).
• Automated repair of binary and assembly programs for cooperating embedded devices E. Schulte, J. DiLorenzo, W. Weimer, S. Forrest Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS (2013).
• A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8.00 each C. Le Goues, M. Dewey-Vogt, S. Forrest and W. Weimer. International Conference on Software Engineering (ICSE) (2012)
• Representations and operators for omproving evolutionary software repair C. Le Goues, W. Weimer, S. Forrest. Genetic and Evolutionary Computation Conference (2012).
• GenProg: Automatic bug correction in real programs C. Le Goues, T. Nguyen, S. Forrest, W. Weimer. ACM Transactions on Software Engineering 38:1 (2012)
• Automatic program repair with evolutionary computation W. Weimer, S. Forrest, C. Le Goues, T. Nguyen. Communications of the ACM Research Highlight 53:5 pp. 109-116 (2010)
• Automated program repair through the evolution of assembly code E. Schulte, S. Forrest, and W. Weimer. 25nd IEEE/ACM International Conference on Automated Software Engineering (ASE) (2010)
• Designing better fitness functions for automated program repair E. Fast, C. Le Goues, W. Weimer, and S. Forrest. Genetic and Evolutionary Computation Conference (GECCO) (2010)
• A genetic programming approach to automated software repair S. Forrest T. Nguyen, W. Weimer and C. Le Goues. /GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation/, pp. 947-954 (2009). Best Paper in Genetic Programming Track.
• Automatically finding patches using genetic programming W. Weimer, T. Nguyen, C. Le Goues and S. Forrest. 31st International Conference on Software Engineering (ICSE) (2009). Winner of ACM SIGSOFT Distinguished Paper Award and IFIP TC2 Manfred Paul Award for Excellence in Software: Theory and Practice.
• Perspectives on Adaptation in Natural and Artificial Systems L. Booker, S. Forrest, M. Mitchell and R. Riolo, Oxford University Press (2005).
• Genetic Algorithms for Finding Polynomial Orderings Jurgen Giesl, Fernando Esponda, and Stephanie Forrest. (2001)
• Learning Classifier Systems J. H. Holland, L.B. Booker, M. Colombetti, M. Dorigo, S. Forrest, D. G. Goldberg, R. L. Riolo, R. E. Smith, P. L. Lanzi, W. Stolzmann, S. W. Wilson Eds. Springer Verlag, pp. 3-32 (2000).
• Fitness Landscapes: Royal Road Functions M. Mitchell and S. Forrest. In Back, Fogel, and Michalewicx (Eds.) Handbook of Evolutionary Computation. Institute of Physics Publishing, Phiuladelphia and Bristol UK, B2.7:1-25 (1997).
• Genetic Algorithms S. Forrest. Computing Surveys Vol. 28:1, pp. 77-80 (1996).
• Genetic operators for the DNA fragment-assembly problem R. Parsons, S. Forrest, and C. Burks. Machine Learning Vol. 21:1/2, pp. 11-33 (1995). Abstract
• Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms T. Jones and S. Forrest. In L.J. Eshelman (Ed.) Proc. of the Sixth Int. Conf. on Genetic Algorithms Morgan Kaufmann, San Francisco, CA, pp. 184-192 (1995). Abstract
• Genetic algorithms and artificial life M. Mitchell and S. Forrest. Artificial Life, Vol. 1, No. 3 (1994), pp. 267-289. Reprinted in C. G. Langton (Ed.) Artificial Life: an Overview, MIT Press, Cambridge, MA (1995). Abstract
• When will a genetic algorithm outperform hill climbing? M. Mitchell, J.H. Holland, and S. Forrest. In J.D. Cowan, G. Tesauro, and J. Alspector, (eds.), Advances in Neural Information Processing Systems, Vol. 6, San Mateo, CA: Morgan Kaufmann (1994).
• "Genetic algorithms: Principles of adaptation applied to computation." S. Forrest. Science, Vol. 261, Aug. 1993, pp. 872-878. Science Reprints
• Using genetic algorithms to explore pattern recognition in the immune system S. Forrest, B. Javornik, R.E. Smith, and A.S. Perelson. Evolutionary Computation, Vol. 1, No. 3 (1993), pp. 191-211. Abstract
• "What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation." S. Forrest and M. Mitchell. Machine Learning, Vol. 13, No. 2/3 (1993).
• "Towards a stronger building-blocks hypothesis: Effects of relative building-block fitness on GA performance." S. Forrest and M. Mitchell. In Proceedings of a Workshop on Foundations of Genetic Algorithms, Los Altos, CA: Morgan Kaufmann (1993).
• "Genetic algorithms for DNA sequence assembly." R. Parsons, S. Forrest, and C. Burks. In L. Hunter, et al., (eds.), /Proceedings of the First International Conference on Intelligent Systems for Molecular Biology/, Menlo Park, CA: AAAI/MIT Press (1993).
• Searching for diverse, cooperative populations with genetic algorithms R.E. Smith, S. Forrest, and A.S. Perelson. Evolutionary Computation, Vol. 1, No. 2, pp. 127-149 (1993). Abstract
• "The royal road for genetic algorithms: Fitness landscapes and GA performance." M. Mitchell, S. Forrest, and J.H. Holland. In Proceedings of the First European Conference on Artificial Life, Cambridge, MA: MIT Press (1992).
• "Using genetic algorithms for controller design: Simultaneous stabilization and eigenvalue placement in a region." W.E. Schmitendorf, O. Shaw, R. Benson, and S. Forrest. In Proceedings of AIAA Guidance Navigation and Control Conference, Hilton Head, SC, Aug. 1992.
• "Genetic algorithms, nonlinear dynamical systems, and global stability models." S. Forrest and G. Mayer-Kress. In L. Davis, (ed.), The Handbook of Genetic Algorithms, New York, NY: Van Nostrand Reinhold (1991).

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