Daniel R. Tauritz
John Woodward
- John Woodward. Computable and Incomputable Search Algorithms and Functions. IEEE International Conference on Intelligent Computing and Intelligent Systems (IEEE ICIS 2009), pp. 871--875, 2009.Google Scholar
- John Woodward. The Necessity of Meta Bias in Search Algorithms. International Conference on Computational Intelligence and Software Engineering (CiSE), pp. 1--4, 2010.Google Scholar
- John Woodward & Ruibin Bai. Why Evolution is not a Good Paradigm for Program Induction: A Critique of Genetic Programming. In Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 593--600, 2009.Google ScholarDigital Library
- Jerry Swan, John Woodward, Ender Ozcan, Graham Kendall, Edmund Burke. Searching the Hyper-heuristic Design Space. Cognitive Computation, 6:66--73, 2014.Google ScholarCross Ref
- Gisele L. Pappa, Gabriela Ochoa, Matthew R. Hyde, Alex A. Freitas, John Woodward, Jerry Swan. Contrasting meta-learning and hyper-heuristic research. Genetic Programming and Evolvable Machines, 15:3--35, 2014.Google ScholarDigital Library
- Edmund K. Burke, Matthew Hyde, Graham Kendall, and John Woodward. Automating the Packing Heuristic Design Process with Genetic Programming. Evolutionary Computation, 20(1):63--89, 2012.Google ScholarDigital Library
- Edmund K. Burke, Matthew R. Hyde, Graham Kendall, and John Woodward. A Genetic Programming Hyper-Heuristic Approach for Evolving Two Dimensional Strip Packing Heuristics. IEEE Transactions on Evolutionary Computation, 14(6):942--958, 2010.Google ScholarDigital Library
- Edmund K. Burke, Matthew R. Hyde, Graham Kendall, Gabriela Ochoa, Ender Ozcan and John R. Woodward. Exploring Hyper-heuristic Methodologies with Genetic Programming, Computational Intelligence: Collaboration, Fusion and Emergence, In C. Mumford and L. Jain (eds.), Intelligent Systems Reference Library, Springer, pp. 177--201, 2009.Google Scholar
- Edmund K. Burke, Matthew Hyde, Graham Kendall and John R. Woodward. The Scalability of Evolved On Line Bin Packing Heuristics. In Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2530--2537, 2007.Google Scholar
- R. Poli, John R. Woodward, and Edmund K. Burke. A Histogram-matching Approach to the Evolution of Bin-packing Strategies. In Proceedings of the IEEE Congress on Evolutionary Computation, pp. 3500--3507, 2007.Google ScholarCross Ref
- Edmund K. Burke, Matthew Hyde, Graham Kendall, and John Woodward. Automatic Heuristic Generation with Genetic Programming: Evolving a Jack-of-all-Trades or a Master of One, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1559--1565, 2007.Google ScholarDigital Library
- John R. Woodward and Jerry Swan. Template Method Hyper-heuristics, Metaheuristic Design Patterns (MetaDeeP) workshop, GECCO Comp'14, pp. 1437--1438, 2014.Google Scholar
- Saemundur O. Haraldsson and John R. Woodward, Automated Design of Algorithms and Genetic Improvement: Contrast and Commonalities, 4th Workshop on Automatic Design of Algorithms (ECADA), GECCO Comp '14, pp. 1373--1380, 2014.Google Scholar
- John R. Woodward, Simon P. Martin and Jerry Swan. Benchmarks That Matter For Genetic Programming, 4th Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA), GECCO Comp '14, pp. 1397--1404, 2014.Google Scholar
- John R. Woodward and Jerry Swan. The Automatic Generation of Mutation Operators for Genetic Algorithms, 2nd Workshop on Evolutionary Computation for the Automated Design of Algorithms (ECADA), GECCO Comp' 12, pp. 67--74, 2012.Google Scholar
- John R. Woodward and Jerry Swan. Automatically Designing Selection Heuristics. 1st Workshop on Evolutionary Computation for Designing Generic Algorithms, pp. 583--590, 2011.Google Scholar
- Edmund K. Burke, Matthew Hyde, Graham Kendall, Gabriela Ochoa, Ender Ozcan, and John Woodward. A Classification of Hyper-heuristics Approaches, Handbook of Metaheuristics, pp. 449--468, International Series in Operations Research & Management Science, M. Gendreauand J-Y Potvin (Eds.), Springer, 2010.Google ScholarCross Ref
- Libin Hong and John Woodward and Jingpeng Li and Ender Ozcan. Automated Design of Probability Distributions as Mutation Operators for Evolutionary Programming Using Genetic Programming. Proceedings of the 16th European Conference on Genetic Programming (EuroGP 2013), volume 7831, pp. 85--96, 2013.Google ScholarDigital Library
- Ekaterina A. Smorodkina and Daniel R. Tauritz. Toward Automating EA Configuration: the Parent Selection Stage. In Proceedings of CEC 2007 - IEEE Congress on Evolutionary Computation, pp. 63--70, 2007.Google Scholar
- Brian W. Goldmanand Daniel R. Tauritz. Self-Configuring Crossover. In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '11), pp. 575--582, 2011.Google Scholar
- Matthew A. Martin and Daniel R. Tauritz. Evolving Black-Box Search Algorithms Employing Genetic Programming. In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '13), pp. 1497--1504, 2013.Google Scholar
- Nathaniel R. Kamrath, Brian W. Goldmanand Daniel R. Tauritz. Using Supportive Coevolution to Evolve Self-Configuring Crossover. In Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '13), pp. 1489--1496, 2013.Google ScholarDigital Library
- Matthew A. Martin and Daniel R. Tauritz. A Problem Configuration Study of the Robustness of a Black-Box Search Algorithm Hyper-Heuristic. In Proceedings of the 16th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '14), pp. 1389--1396, 2014.Google Scholar
- Sean Harris, Travis Bueter, and Daniel R. Tauritz. A Comparison of Genetic Programming Variants for Hyper-Heuristics. In Proceedings of the 17th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '15), pp.1043--1050, 2015.Google ScholarCross Ref
- Matthew A. Martin and Daniel R. Tauritz. Hyper-Heuristics: A Study On Increasing Primitive-Space. In Proceedings of the 17th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '15), pp. 1051--1058, 2015.Google Scholar
- Alex R. Bertels and Daniel R. Tauritz. Why Asynchronous Parallel Evolution is the Future of Hyper-heuristics: A CDCL SAT Solver Case Study. In Proceedings of the 18th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '16), pp. 1359--1365, 2016.Google Scholar
- Aaron S. Pope, Daniel R. Tauritz and Alexander D. Kent. Evolving Random Graph Generators: A Case for Increased Algorithmic Primitive Granularity. In Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), 2016.Google ScholarCross Ref
- Aaron S. Pope, Daniel R. Tauritz and Alexander D. Kent. Evolving Multi-level Graph Partitioning Algorithms. In Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016), 2016.Google Scholar
- Islam Elnabarawy, Daniel R. Tauritz, Donald C. Wunsch. Evolutionary Computation for the Automated Design of Category Functions for Fuzzy ART: An Initial Exploration. In Proceedings of the 19th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO'17), pp. 1133--1140, 2017.Google ScholarDigital Library
- Adam Harter, Daniel R. Tauritz, William M. Siever. Asynchronous Parallel Cartesian Genetic Programming. In Proceedings of the 19th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO'17), pp. 1820--1824, 2017.Google ScholarDigital Library
- Marketa Illetskova, Alex R. Bertels, Joshua M. Tuggle, Adam Harter, Samuel Richter, Daniel R. Tauritz, Samuel Mulder, Denis Bueno, Michelle Leger and William M. Siever. Improving Performance of CDCL SAT Solvers by Automated Design of Variable Selection Heuristics. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI 2017), 2017.Google ScholarCross Ref
- John R. Woodward, Jerry Swan, "Why classifying search algorithms is essential", Progress in Informatics and Computing (PIC) 2010 IEEE International Conference on, vol. 1, pp. 285--289, 2010.Google ScholarCross Ref
- Samuel N. Richter and Daniel R. Tauritz. The Automated Design of Probabilistic Selection Methods for Evolutionary Algorithms. In Proceedings of the 20th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO 2018), pp. 1545--1552, 2018.Google Scholar
- Aaron Scott Pope, Robert Morning, Daniel R. Tauritz, and Alexander D. Kent. Automated Design of Network Security Metrics. In Proceedings of the 20th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO 2018), pp. 1680--1687, 2018.Google Scholar
- John R. Woodward and Ruibin Bai. Canonical representation genetic programming. In Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 585--592, 2009.Google Scholar
- Saemundur O. Haraldsson, John R. Woodward, Alexander EI Brownlee, and Kristin Siggeirsdottir. Fixing bugs in your sleep: How genetic improvement became an overnight success. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1513--1520, 2017.Google ScholarDigital Library
- Aaron Scott Pope, Daniel R. Tauritz, and Melissa Turcotte. Automated Design of Tailored Link Prediction Heuristics for Applications in Enterprise Network Security. In Proceedings of the 21st Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '19), pp. 1634--1642, 2019.Google ScholarDigital Library
- Adam Harter, Aaron Scott Pope, Daniel R. Tauritz, and Chris Rawlings. Empirical Evidence of the Effectiveness of Primitive Granularity Control for Hyper-Heuristics. In Proceedings of the 21st Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '19), pp. 1478--1486, 2019.Google Scholar
- Aaron Scott Pope, Daniel R. Tauritz, and Chris Rawlings. Automated Design of Random Dynamic Graph Models. In Proceedings of the 21st Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '19), pp. 1504--1512, 2019.Google Scholar
- Samuel N. Richter, Michael G. Schoen, and Daniel R. Tauritz. Evolving Mean-Update Selection Methods for CMA-ES. In Proceedings of the 21st Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '19), pp. 1513--1517, 2019.Google ScholarDigital Library
- Aaron Scott Pope and Daniel R. Tauritz. Automated Design of Multi-Level Network Partitioning Heuristics Employing Self-Adaptive Primitive Granularity Control. In Proceedings of the 22nd Annual Conference on Genetic and Evolutionary Computation (GECCO '20), 1168--1176, Cancún, Mexico, July 8-12, 2020.Google ScholarDigital Library
- Braden N. Tisdale, Aaron Scott Pope, and Daniel R. Tauritz. Dynamic Primitive Granularity Control: An Exploration of Unique Design Considerations. In Proceedings of the 22nd Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '20), pages 1906--1914, Cancún, Mexico, July 8-12, 2020.Google ScholarDigital Library
- Nathaniel R. Kamrath, Aaron Scott Pope, and Daniel R. Tauritz. The Automated Design of Local Optimizers for Memetic Algorithms Employing Supportive Coevolution. In Proceedings of the 22nd Annual Conference Companion on Genetic and Evolutionary Computation (GECCO '20), pages 1889--1897, Cancún, Mexico, July 8-12, 2020.Google ScholarDigital Library
- Hyper-heuristics tutorial
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