Scaling of probability-based optimization algorithms

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Shapiro, J L ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

INVERSE PROBLEMS;

CONSISTENT PERFORMANCE; DETAILED BALANCE; INVERSE SYSTEM; LEARNING RATES; OPTIMIZATION ALGORITHMS; POPULATION-BASED INCREMENTAL LEARNING; SMOOTH FUNCTIONS; SQUARE ROOTS;

LEARNING ALGORITHMS;

EID: 84898960693     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

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3. P. Larrañaga and J. A. Lozano. Estimation of Distribution Algorithms, A New Tool for Evolutionary Computation. Kluwer Academic Publishers, 2001.
4. Eckhard Limpert, Werner A. Stahel, and Markus Abbt. Log-normal distributions across the sciences: Keys and clues. BioScience, 51(5):341-352, 2001.
5. H. Mühlenbein. The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3):303-346, 1997.
6. M. Pelikan, D. E. Goldberg, and F. Lobo. A survey of optimization by building and using probabilistic models. Technical report, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, 1999.
7. Jonathan L. Shapiro and Adam Prügel-Bennett. Maximum entropy analysis of genetic algorithm operators. Lecture Notes in Computer Science, 993:14-24, 1995.