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The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination
edited_book
Author(s):
Larry J. Eshelman
Publication date
(Print):
1991
Publisher:
Elsevier
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Genomic Prediction: Genetic Gain
Author and book information
Book Chapter
Publication date (Print):
1991
Pages
: 265-283
DOI:
10.1016/B978-0-08-050684-5.50020-3
SO-VID:
a632b0a3-0767-4816-b12e-cdebece56066
License:
https://www.elsevier.com/tdm/userlicense/1.0/
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Book chapters
pp. 23
Epistasis Variance: A Viewpoint on GA-Hardness
pp. 53
An Extension To the Theory of Convergence and a Proof of the Time Complexity of Genetic Algorithms
pp. 69
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
pp. 94
A Study of Reproduction in Generational and Steady-State Genetic Algorithms
pp. 102
Spurious Correlations and Premature Convergence in Genetic Algorithms
pp. 205
Genetic Algorithms for Real Parameter Optimization
pp. 221
Fundamental Principles of Deception in Genetic Search
pp. 265
The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination
pp. 284
Genetic Operators for Sequencing Problems
pp. 301
An Analysis of Multi-Point Crossover
pp. 316
Evolution in Time and Space – The Parallel Genetic Algorithm
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