From Cells to Islands: An unified Model of Cellular Parallel Genetic Algorithms [PDF]
Dans ACRI 2006, 7th International Conference - 7th International
Conference on Cellular Automata For Research and Industry - ACRI 2006,
Perpignan : France (2006), 2008 This paper presents the Anisotropic selection scheme for cellular Genetic Algorithms (cGA). This new scheme allows to enhance diversity and to control the selective pressure which are two important issues in Genetic Algorithms, especially when trying to solve difficult optimization problems.
arxiv
Evolving Shepherding Behavior with Genetic Programming Algorithms [PDF]
arXiv, 2016 We apply genetic programming techniques to the `shepherding' problem, in which a group of one type of animal (sheep dogs) attempts to control the movements of a second group of animals (sheep) obeying flocking behavior. Our genetic programming algorithm evolves an expression tree that governs the movements of each dog. The operands of the tree are hand-
arxiv
Asynchronous Transmission of Wireless Multicast System with Genetic Joint Antennas Selection [PDF]
IEEE WCNC 2013 workshop on Convergence of Broadcasting and
Broadband Communications. Shanghai, China: April , pp.1-5, 2014 Optimal antenna selection algorithm of multicast transmission can significantly reduce the number of antennas and can acquire lower complexity and high performance which is close to that of exhaustive search. An asynchronous multicast transmission mechanism based on genetic antenna selection is proposed.
arxiv
Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data [PDF]
, 2004 Roger M. Jarvis, Royston Goodacre
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A genetic algorithm for structure-activity relationships: software implementation [PDF]
arXiv, 2009 The design and the implementation of a genetic algorithm are described. The applicability domain is on structure-activity relationships expressed as multiple linear regressions and predictor variables are from families of structure-based molecular descriptors.
arxiv
The quasispecies regime for the simple genetic algorithm with ranking selection [PDF]
arXiv, 2014 We study the simple genetic algorithm with a ranking selection mechanism (linear ranking or tournament). We denote by $\ell$ the length of the chromosomes, by $m$ the population size, by $p_C$ the crossover probability and by $p_M$ the mutation probability.
arxiv
An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray Data [PDF]
, 2006 Michael Lecocke, Kenneth R. Hess
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Efficient Population Diversity Handling Genetic Algorithm for QoS-Aware Web Services Selection [PDF]
, 2006 Chengwen Zhang, Sen Su, Junliang Chen
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Genetic Algorithm Based Feature Selection Method Development for Pattern Recognition [PDF]
, 2006 Chang-Hyun Park +3 more
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A new genetic algorithm specifically based on mutation and selection [PDF]
, 2007 Laure Rigal, Laurent Truffet
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