Results 221 to 230 of about 263,270 (261)
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Neural Computing and Applications, 2009
Random individual initialization tends to generate too many eccentric and homogeneous individuals which cause slow and premature convergence. It needs many operations (selection strategy, incest prevention and mutation) to fix, which consume too much computation and lose many good genes.
Qijian Chen +2 more
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Random individual initialization tends to generate too many eccentric and homogeneous individuals which cause slow and premature convergence. It needs many operations (selection strategy, incest prevention and mutation) to fix, which consume too much computation and lose many good genes.
Qijian Chen +2 more
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Hierarchical Genetic Algorithms
2004Current Genetic Algorithms can efficiently address order-k separable problems, in which the order of the linkage is restricted to a low value k. Outside this class, there exist hierarchical problems that cannot be addressed by current genetic algorithms, yet can be addressed efficiently in principle by exploiting hierarchy.
de Jong, E.D. +2 more
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On coevolutionary genetic algorithms
Soft Computing, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2010 Seventh International Conference on Information Technology: New Generations, 2010
This paper proposes a new genetic algorithm called a meiosis genetic algorithm (MGA) based on meiosis cell division. The meiosis cell division serves producing chromosome diversity which speeds up converging to a mature solution chromosome. The experiment results on a knapsack problem using the new algorithm show that it is more efficient than the ...
Nattavut Wiriyasermkul +2 more
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This paper proposes a new genetic algorithm called a meiosis genetic algorithm (MGA) based on meiosis cell division. The meiosis cell division serves producing chromosome diversity which speeds up converging to a mature solution chromosome. The experiment results on a knapsack problem using the new algorithm show that it is more efficient than the ...
Nattavut Wiriyasermkul +2 more
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Information Management & Computer Security, 2004
Describes one of the newest forms of artificial intelligence being applied to the solution of business problems – the genetic algorithm (GA). GAs are useful when a problem has multiple solutions, some of which are better than others. Unlike deterministic, linear and non‐linear optimization models, GAs test a variety of solutions and, through an ...
Fritz H. Grupe, Simon Jooste
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Describes one of the newest forms of artificial intelligence being applied to the solution of business problems – the genetic algorithm (GA). GAs are useful when a problem has multiple solutions, some of which are better than others. Unlike deterministic, linear and non‐linear optimization models, GAs test a variety of solutions and, through an ...
Fritz H. Grupe, Simon Jooste
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Genetic algorithms and their applications
IEEE Signal Processing Magazine, 1996This article introduces the genetic algorithm (GA) as an emerging optimization algorithm for signal processing. After a discussion of traditional optimization techniques, it reviews the fundamental operations of a simple GA and discusses procedures to improve its functionality.
Kit-Sang Tang +3 more
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Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 2002
Comparative studies of sets of control parameter values are commonly performed when tuning an evolutionary algorithm for a class of problem instances. The standard approach is to identify the most useful set of control parameter settings for a domain. In this paper, we propose an alternative anytime algorithm portfolio technique in which computational ...
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Comparative studies of sets of control parameter values are commonly performed when tuning an evolutionary algorithm for a class of problem instances. The standard approach is to identify the most useful set of control parameter settings for a domain. In this paper, we propose an alternative anytime algorithm portfolio technique in which computational ...
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Homogeneous genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2007A new type of genetic algorithm (GA) is developed to mitigate one or both of the following two major difficulties that traditional GAs may suffer: (1) when the number of 'active genes' needs to be held constant or kept within some prescribed range, and (2) when the set of genes is much larger than the set of active genes of feasible solutions under ...
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An introduction to genetic algorithms
Sadhana, 1999Genetic algorithms (GAs) are search and optimization tools, which work differently compared to classical search and optimization methods. Because of their broad applicability, ease of use, and global perspective, GAs have been increasingly applied to various search and optimization problems in the recent past.
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European Journal of Operational Research, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu-Chiun Chiou, Lawrence W. Lan
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu-Chiun Chiou, Lawrence W. Lan
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