Lee Graham
ABSTRACT
Designing efficient sorting networks has been a challenging combinatorial optimization problem since the early 1960's. The application of evolutionary computing to this problem has yielded human-competitive results in recent years. We build on previous work by presenting a genetic algorithm whose parameters and heuristics are tuned on a small instance of the problem, and then scaled up to larger instances. Also presented are positive and negative results regarding the efficacy of several domain-specific heuristics.
- Sung-Soon Choi and Byung-Ro Moon. A New Approach to the Sorting Network Problem Evolving Parallel Layers. In Proceedings of GECCO-2001. Morgan Kaufmann, 2001, pp. 258--265.Google Scholar
- Sung-Soon Choi and Byung-Ro Moon. Isomorphism, Normalization and a Genetic Algorithm for Sorting Networks. In Proceedings of GECCO-2002. Morgan Kaufmann, 2002, pp. 327--334. Google ScholarDigital Library
- Sung-Soon Choi and Byung-Ro Moon. More Effective Genetic Search for the Sorting Network Problem. In Proceedings of GECCO-2002. Morgan Kaufmann, 2002, pp. 335--342. Google ScholarDigital Library
- Harrison, M. L., and Foster, J. A. Co-evolving Faults to Improve the Fault Tolerance of Sorting Networks. In Proceedings of EuroGP 2004. Springer-Verlag, 2004.Google Scholar
- Danny Hillis. Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure. In Proceedings of Artificial Life II (1990). Westview Press, 1991.Google Scholar
- Hugues Juillé. Evolution of Non-deterministic Incremental Algorithms as a New Approach for Search in State Spaces. In Proceedings of ICGA-95. Morgan Kaufmann, 1995, pp. 351--358. Google ScholarDigital Library
- Donald Knuth. The Art of Computer Programming, Volume 3: Sorting and Searching (2nd edition). Addison Wesley, 1998. Google ScholarDigital Library
- Sekanina Lukás. Evolving Constructors for Infinitely Growing Sorting Networks and Medians. In SOFSEM: Theory and Practice of Computer Science. Springer, 2004, pp. 314--323.Google Scholar
- Marek Piotrów. Depth Optimal Sorting Networks Resistant to k Passive Faults. SIAM Journal on Computing, Volume 33, Number 6 (2004), pp. 1484--1512. Google ScholarDigital Library
Index Terms
- Statistical analysis of heuristics for evolving sorting networks
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