Most of my recent teaching has been in introductory computer science and advanced courses in artificial intelligence. In all of my courses I encourage students to work on projects related to their own interests, to conduct original research, and to explore connections between the material that we are covering and work in other disciplines across the liberal arts curriculum. I also teach courses that involve both computation and aspects of other disciplines such as cognitive science, social science, physics, evolutionary biology, and the arts. Beyond my courses, I direct an initiative on Artificial Intelligence in the Liberal Arts.
My primary research area is artificial intelligence, with a particular focus on computational methods that incorporate ideas from evolutionary biology. My interests are broader, however, and I have published in disciplines including psychology, philosophy, neuroscience, animal behavior, physics, mathematics, and engineering.
Within evolutionary computation, I work primarily on genetic programming, which leverages random variation and selection to produce computer programs that solve specified problems. My students and I have made contributions both to the development of new genetic programming techniques and to the application of those techniques to difficult problems. Among the former are the Push programming language, which is a language not for human programmers but for evolutionary processes, and the lexicase selection algorithm for determining which programs should be reproduced, mutated, and recombined in order to solve problems most reliably and quickly. We have worked on applications in areas ranging from pure mathematics and quantum computer programming to wind turbine modeling and music composition.
I also work on “Artificial Life” investigations into the emergence of behavior within ecosystems, human/machine collaboration on creative tasks, DNA computing, the modeling of dog and wolf behavior, the game theory of altruism and cooperation, the developmental neuroscience of human planning, knowledge representation for web agents, and the philosophy of computational theories of mind.
Pantridge, E., T. Helmuth, and L. Spector. 2022. Functional code building genetic programming. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '22), pp. 1000-1008. Published by the Association for Computing Machinery.
Ding, L., and L. Spector. 2022. Evolutionary quantum architecture search for parametrized quantum circuits. In GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 2190--2195. Published by the Association for Computing Machinery.
Ding, L., and L. Spector. 2022. Optimizing Neural Networks with Gradient Lexicase Selection. In The Tenth International Conference on Learning Representations (ICLR 2022). Published at openreview.net: https://openreview.net/pdf?id=J_2xNmVcY4
Helmuth, T., and L. Spector. 2021. Problem-solving benefits of down-sampled lexicase selection. In Artificial Life, Vol. 27, Issue 3-4, pp. 183-203.
Helmuth, T., Pantridge, E., and L. Spector. 2020. On the importance of specialists for lexicase selection. In Genetic Programming and Evolvable Machines,Vol. 21, pp. 349-373.
Banzhaf, W., L. Spector, and L. Sheneman, editors. 2019. Genetic Programming Theory and Practice XVI. New York: Springer.
La Cava, W., T. Helmuth, L. Spector, and J. H. Moore. 2018. A Probabilistic and Multi-Objective Analysis of Lexicase Selection and ε-Lexicase Selection. In Evolutionary Computation, Vol. 27, Issue 3, pp. 377-402.
Clark, D. M., and L. Spector. 2018. Evolution of algebraic terms 3: Term continuity and beam algorithms. In International Journal of Algebra and Computation, Vol. 28, No. 05, pp. 759-790.
McCaffrey, T., and L. Spector. 2017. An approach to human-machine collaboration in innovation. In AI-EDAM: Artificial Intelligence for Engineering Design, Analysis and Manufacturing.
DelRosso, N. V., S. Hews, L. Spector, and N. D. Derr. 2017. A Molecular Circuit Regenerator to Implement Iterative Strand Displacement Operations. In Angewandte Chemie International Edition.
La Cava, W., K. Danai, L. Spector, P. Fleming, A. Wright, and M. Lackner. 2016. Automatic identification of wind turbine models using evolutionary multiobjective optimization. In Renewable Energy, Volume 87, Part 2, pp. 892-902.
Escobedo, R., C. Muro, L. Spector, and R. P. Coppinger. 2014. Group size, individual role differentiation and effectiveness of cooperation in a homogeneous group of hunters. In Journal of the Royal Society Interface, Vol. 11, No. 95, 20140204, pp. 1-10.
Helmuth, T., L. Spector, and J. Matheson. 2014. Solving Uncompromising Problems with Lexicase Selection. In IEEE Transactions on Evolutionary Computation.
Helmuth, T., and L. Spector. 2013. Evolving SQL Queries from Examples with Developmental Genetic Programming. In Genetic Programming Theory and Practice X, edited by R. L. Riolo, M. Ritchie, J. Moore, and E. Vladislavleva, pp. 1-14. New York: Springer.
Coppinger, R., L. Spector, and L. Miller. 2010. What, if anything, is a Wolf? In The World of Wolves: New Perspectives on Ecology, Behaviour and Management, edited by M. Musiani, L. Boitani and P. Paquet. Calgary: The University of Calgary Press.
Niekum, S., A. Barto, and L. Spector. 2010. Genetic Programming for Reward Function Search. In IEEE Transactions on Autonomous Mental Development, Vol. 2, No. 2, pp. 83-90.
Klein, J., and L. Spector. 2009. 3D Multi-Agent Simulations in the breve Simulation Environment. In Artificial Life Models in Software, 2nd edition, edited by A. Adamatzky and M. Komosinski, pp. 79-106. New York: Springer-Verlag.
Spector, L. 2006. Evolution of Artificial Intelligence. In Artificial Intelligence, Vol. 170, Issue 18, pp. 1251-1253.
Grafman, J., L. Spector, and M.J. Rattermann. 2005. Planning and the Brain. In The Cognitive Psychology of Planning, edited by R. Morris and G. Ward, pp. 181-198. New York, NY: Psychology Press (Taylor & Francis Group).
Spector, L. 2004/2007. Automatic Quantum Computer Programming: A Genetic Programming Approach. Boston, MA: Kluwer Academic Publishers. (Paperback edition published by Springer Science+Business Media, 2007).
Spector, L. 2003. An Essay Concerning Human Understanding of Genetic Programming. In Genetic Programming: Theory and Practice, edited by R.L Riolo and W. Worzel, pp. 11-24. Boston, MA: Kluwer Academic Publishers.
Spector, L. 2002. Hierarchy Helps it Work That Way. In Philosophical Psychology, Vol. 15, No. 2 (June, 2002), pp. 109-117.
Spector, L., and A. Robinson. 2002. Genetic Programming and Autoconstructive Evolution with the Push Programming Language. In Genetic Programming and Evolvable Machines, Vol. 3, No. 1, pp. 7-40.
Rattermann, M.J., L. Spector, J. Grafman, H. Levin, and H. Harward. 2001. Partial and total-order planning: evidence from normal and prefrontally damaged populations. In Cognitive Science, Vol. 25, No. 6 (November/December, 2001), pp. 941-975.
Barnum, H., H.J. Bernstein, and L. Spector. 2000. Quantum circuits for OR and AND of ORs. Journal of Physics A: Mathematical and General, Vol. 33 No. 45 (17 November 2000), pp. 8047-8057.
Spector, L., U.-M. O'Reilly, W. Langdon, and P. Angeline, editors. 1999. Advances in Genetic Programming, Volume 3. Cambridge, MA: MIT Press.
Spector, L. 1995. Artificial Intelligence as the Liberal Arts of Computer Science. In SIGART Bulletin: Special Issue on AI Education. Volume 6, Number 2, pp. 8-10. The Association for Computing Machinery.
Spector, L. and J. Grafman. 1994. Planning, Neuropsychology, and Artificial Intelligence: Cross-Fertilization. In Handbook of Neuropsychology, Volume 9, edited by F. Boller, and J. Grafman, 377-392. Amsterdam: Elsevier Science Publishers B.V.