- 1.Sara Baase. Computer Algorithms: lntrod, ct'ion to Design and Anaylsis. Addison- \.Vesl~,y l)ul)lishillg Conll)a!ly, Reading, MA, 1978.]] Google ScholarDigital Library
- 2.Lawrence Davis. Handbook of Genetic Algorith~l,s. Van Nostrand Reinhold, New York, NY, 1991.]]Google Scholar
- 3.David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machnie Learn- 2rig. Addison-Wesley Ptlhlishiiig Conll'~aily, I}~c., ReadiBlg, N'IA, 1989.]] Google ScholarDigital Library
- 4.David E. Goldberg and R. Lingle. Alleles, loci, and the traveling salesman problem. In John J. Grefenstette, editor, Proceeding of an Jnlern. altonal Conference on Morgan Kaufituan publishers. Inc. 1987.]] Google ScholarDigital Library
- 5.John H. Holland. Adaptation in Natural and Artifical Systems. The University of Michigan Press. Ann Arbor, MI, 1975.]] Google ScholarDigital Library
- 6.Ellis Horowitz and Sartaj Salmi. Fundamentals of Computer Algorithms. Coillputer Science Press, Inc., Rockville, MD, 1978.]] Google ScholarDigital Library
- 7.Kenneth A. De Jong and William M. Spears. Using genetic alrorithms to solve up-complete problems. n J. David Schaffer. editor. Proceedings of the Thrid International Conference on genetic Algorithms, pages 124-132.]] Google ScholarDigital Library
- 8.Christos H. Papadimitrion and Kenneth Steiglitz. Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall, Englewood Cliffs, NJ, 1082.]] Google ScholarDigital Library
- 9.D. Whitney and J. Kauth. Genitor: A different genetic algorithm. In Proceedings of the Rocky Mountain Conference on Artificial Intelligence, pages 118-130. Denver, COm 1988.]]Google Scholar
Index Terms
- Solving the n-queens problem using genetic algorithms
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