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Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences
book

Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences

by Albert Y. Zomaya, Fikret Ercal, Stephan Olariu
October 2000
Intermediate to advanced
288 pages
9h 22m
English
Wiley-Interscience
Content preview from Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences

REFERENCES

1. F. Abbattista, N. Abbattista, and L. Caponetti, An evolutionary and cooperative agent model for optimization. In IEEE International Conference on Evolutionary Computation (ICEC'95), pp. 668–671, Perth, Australia, Dec. 1995.

2. D. Abramson, P. Logothetis, A. Postula, and M. Randall, Application specific computers for combinatorial optimisation. In The Australian Computer Architecture Workshop, Sydney, Feb. 1997.

3. D. A. Abramson, A very high speed architecture to support simulated annealing. IEEE Computer, 25:27–34, May 1992.

4. V. Bachelet, Z. Hafidi, P. Preux, and E.-G. Talbi, Diversifying tabu search by genetic algorithms. In INFORMS'98 on Operations Research and Management Sciences meeting, Montreal, Canada, Apr. 1998.

5. V. Bachelet, P. Preux, and E. G. Talbi, Hybrid parallel heuristics for the quadratic assignment problem. In parallel Optimization Colloquium (POC96), Versailles, France, Mar. 1996.

6. T. Belding, The distributed genetic algorithm revisited. In D. Eshelmaun, ed., Sixth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1995.

7. R. K. Belew, S. McInerny, and N. N. Schraudolph, Evolving networks: Using genetic algorithms with connectionist learning. In C. C. Langton, C. Taylor, S. D. Doyne Farmer, and S. Rasmussen, eds., Second Conference on Artificial Life, pp. 511–548, Addison-Wesley, Reading, MA, 1991.

8. K. D. Boese, A. B. Kahng, and S. Muddu, New adaptive multi-start techniques for combinatorial global optimizations. ...

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Publisher Resources

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