Practical implications of new results in conservation of optimizer performance

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1917, Issue , 2000, Pages 69-78

  English, Thomas M ^a  

^a Tom English Project   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; INFORMATION THEORY; PARALLEL PROCESSING SYSTEMS;

ALGORITHMIC COMPLEXITY; ALGORITHMIC INFORMATION THEORY; ALGORITHMIC INFORMATIONS; FUNCTION OPTIMIZATION; NEW RESULTS; RANDOM VALUES; STATISTICAL INFORMATION; TEST FUNCTIONS;

DISTRIBUTION FUNCTIONS;

EID: 84947941376     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45356-3_7     Document Type: Conference Paper

References (6)

1. Schaffer, C. 1994. "A conservation law for generalization performance," in Proc. Eleventh Intl Conf. on Machine Learning, H. Willian and W. Cohen, eds. San Francisco: Morgan Kaufmann, pp. 259-265.
2. Wolpert, D. H., and W. G. Macready. 1997. "No free lunch theorems for optimization," IEEE Trans. Evolutionary Computation, vol. 1, no. 1, pp. 67-82.
3. English, T. M. 1996. "Evaluation of evolutionary and genetic optimizers: No free lunch," in Evolutionary Programming V: Proc. Fifth Ann. Conf. on Evolutionary Programming, L. Fogel, P. Angeline, and T. Bäck, eds. Cambridge, Mass.: MIT Press, pp. 163-169.
4. English, T. M. To appear. "Optimization is easy and learning is hard in the typical function" Proc. 2000 Congress on Evolutionary Computation.
5. Chaitin, G. J. 1999. The Unknowable, New York: Springer-Verlag, chap. 6.
6. Cover, T. M., and J. A. Thomas. 1991. Elements of Information Theory, New York: Wiley & Sons.