Results 291 to 300 of about 7,593,248 (327)
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Annual Conference Computational Learning Theory, 1995
We analyze the performance of a Genetic Type Algorithm we call Culling and a variety of other algorithms on a problem we refer to as ASP. Culling is near optimal for this problem, highly noise tolerant, and the best known a~~roach . . in some regimes. We
E. Baum, D. Boneh, Charles Garrett
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We analyze the performance of a Genetic Type Algorithm we call Culling and a variety of other algorithms on a problem we refer to as ASP. Culling is near optimal for this problem, highly noise tolerant, and the best known a~~roach . . in some regimes. We
E. Baum, D. Boneh, Charles Garrett
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An introduction to genetic algorithms
, 1996From the Publisher: "This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in ...
Melanie Mitchell
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Agile Artificial Intelligence in Pharo, 2020
This chapter includes the second phase of the Re-Coding Homes Project, which has been conducted as a TUBITAK (The Scientific and Technological Research Council of Turkey) research project with the title “A User-Centered Model Research Towards a Flexible ...
Alexandre Bergel
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This chapter includes the second phase of the Re-Coding Homes Project, which has been conducted as a TUBITAK (The Scientific and Technological Research Council of Turkey) research project with the title “A User-Centered Model Research Towards a Flexible ...
Alexandre Bergel
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Optimization of Control Parameters for Genetic Algorithms
IEEE Transactions on Systems, Man and Cybernetics, 1986The task of optimizing a complex system presents at least two levels of problems for the system designer. First, a class of optimization algorithms must be chosen that is suitable for application to the system.
J. Grefenstette
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Annual Conference on Genetic and Evolutionary Computation, 2017
For genetic algorithms (GAs) using a bit-string representation of length n, the general recommendation is to take 1/n as mutation rate. In this work, we discuss whether this is justified for multi-modal functions.
Benjamin Doerr +3 more
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For genetic algorithms (GAs) using a bit-string representation of length n, the general recommendation is to take 1/n as mutation rate. In this work, we discuss whether this is justified for multi-modal functions.
Benjamin Doerr +3 more
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On the practical genetic algorithms
Annual Conference on Genetic and Evolutionary Computation, 2005This paper offers practical design-guidelines for developing efficient genetic algorithms (GAs) to successfully solve real-world problems. As an important design component, a practical population-sizing model is presented and verified.
C. Ahn, Sanghoun Oh, R. S. Ramakrishna
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Computer, 1994
Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes.
M. Srinivas, L. Patnaik
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Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes.
M. Srinivas, L. Patnaik
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Genetic Algorithms for Pattern Recognition
, 2017From the Publisher: Solving pattern recognition problems involves an enormous amount ofcomputational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and ...
S. Pal, Paul P. Wang
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A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation, 2002Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non ...
K. Deb +3 more
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Analysing mutation schemes for real-parameter genetic algorithms
International Journal of Artificial Intelligence and Soft Computing, 2014Mutation is an important operator in genetic algorithms GAs, as it ensures maintenance of diversity in evolving populations of GAs. Real-parameter GAs RGAs handle real-valued variables directly without going to a binary string representation of variables.
K. Deb, Debayan Deb
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