photo I live and work in San Diego, California.








Current Project

equidistant Constructive Codes – https://constructive.codes

CPro1 uses LLMs to generate code that constructs combinatorial designs.

Rosin, C.D. (2025) “Using Code Generation to Solve Open Instances of Combinatorial Design Problems.” https://arxiv.org/abs/2501.17725

On GitHub: Code and Result Files


Recent Projects

itp2program MAKESPEARE synthesizes short assembly language programs, using a form of local search. In the TIS-100 programming game, it solved the “Image Test Pattern 2” puzzle with a program of just 9 instructions – still shortest on the Reddit TIS-100 Leaderboard.

Rosin, C.D. (2019) Stepping Stones to Inductive Synthesis of Low-Level Looping Programs. AAAI 2019. PDF on arXiv.

On GitHub: Code and Benchmarks



goboard PUCB is a multi-armed bandit algorithm that uses information from a learned predictor to improve decisions.

PUCB paper: Rosin, C.D. (2011). Multi-armed bandits with episode context. Annals of Mathematics and Artificial Intelligence. PDF of the ISAIM 2010 conference version

The PUCB paper was cited by the AlphaGo paper, where PUCB was modified into AlphaGo’s PUCT method for Monte Carlo Tree Search.


sol178 Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo Tree Search (MCTS) method for single-agent problems. It set the record of 178 lines for Morpion Solitaire.

Records page at morpionsolitaire.com.

Morpion Solitaire record via Monte-Carlo search.

Rosin, C.D. (2011). Nested Rollout Policy Adaptation for Monte Carlo Tree Search. IJCAI 2011 (Distinguished Paper). PDF.





Other Publications:

  • Rosin, C.D. (2014). Unweighted stochastic local search can be effective for random CSP benchmarks. arXiv:1411.7480. Link to source code.
  • Rosin, C.D. (2014). Game playing. WIREs Cognitive Science 5:193-205. DOI: 10.1002/wcs.1278. PDF of submitted version. Final version is available here.
  • Rosin, C.D. (2000). “Sample complexity of model-based search.” Journal of Computer and System Sciences 60:278-301.
  • Rosin, C.D., Belew, R.K., Walker, W.L., Morris, G.M., Olson, A.J., Goodsell, D.S. (1999). Coevolution and subsite decomposition for the design of resistance-evading HIV-1 protease inhibitors. Journal of Molecular Biology 287(1):77-92.
  • Rosin, C.D., Belew, R.K., Morris, G.M., Olson, A.J., and Goodsell, D.S. (1999). “Coevolutionary analysis of resistance-evading HIV-1 protease inhibitors.” Proceedings of the National Academy of Sciences 96(4):1369-1374.
  • Rosin, C.D. (1998). “Sample complexity of model-based search.” Proceedings of the Eleventh Annual Conference on Computational Learning Theory. ACM.
  • Rosin, C.D., Belew, R.K., Morris, G.M., Olson, A.J., and Goodsell, D.S. (1998). “Computational coevolution of antiviral drug resistance.” Artificial Life 4:41-59.
  • Rosin, C.D., Belew, R.K., Morris, G.M., Olson, A.J., and Goodsell, D.S. (1998). “Computational coevolution of antiviral drug resistance.” Proceedings of the Sixth International Conference on Artificial Life. MIT Press.
  • Coevolutionary Search Among Adversaries Christopher D. Rosin. Ph.D. Dissertation, University of California, San Diego, 1997.
  • “A Comparison of Global and Local Search Methods in Drug Docking”, Christopher D. Rosin, R. Scott Halliday, William E. Hart, and Richard K. Belew. Submitted version. Final version in the Proceedings of the Seventh International Conference on Genetic Algorithms. An older version of this paper is available as Technical Report #CS97-522.
  • Technical Report #CS96-491: “New methods for Competitive Coevolution”, Christopher D. Rosin and Richard K. Belew. Final version in Evolutionary Computation 5:1.
  • ICGA 95 paper (submitted version): “Methods for Competitive Co-evolution: Finding Opponents Worth Beating”, Christopher D. Rosin and Richard K. Belew. Final version in Proceedings of the Sixth International Conference on Genetic Algorithms. L.J. Eshelman, editor.
  • COLT 96 paper (submitted version): “A Competitive Approach to Game Learning”, Christopher D. Rosin and Richard K. Belew. Final version in Proceedings of the Ninth Annual ACM Conference on Computational Learning Theory.