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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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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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Genetic algorithms and evolution
Journal of Theoretical Biology, 1990The genetic algorithm (GA) as developed by Holland (1975, Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press) is an optimization technique based on natural selection. We use a modified version of this technique to investigate which aspects of natural selection make it an efficient search procedure.
B H, Sumida +3 more
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1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360), 1999
Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA.
Georges R. Harik +2 more
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Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA.
Georges R. Harik +2 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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Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 2002
In this paper, a new genetic symbiosis algorithm (GSA) is proposed based on the symbiotic concept found widely in ecosystems. Since in the conventional genetic algorithms (GA) reproduction is done using only the fitness function of each individual, there are some problems such as premature convergence to an undesirable solution at a very early stage of
Kotaro Hirasawa +4 more
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In this paper, a new genetic symbiosis algorithm (GSA) is proposed based on the symbiotic concept found widely in ecosystems. Since in the conventional genetic algorithms (GA) reproduction is done using only the fitness function of each individual, there are some problems such as premature convergence to an undesirable solution at a very early stage of
Kotaro Hirasawa +4 more
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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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Genetic algorithms in chemometrics
Journal of Chemometrics, 2012This review covers the application of Genetic Algorithms (GAs) in Chemometrics. The first applications of GAs in chemistry date back to the 1970s, and in the last decades, they have been more and more frequently used to solve different kinds of problems, for example, when the objective functions do not possess properties such as continuity ...
A. Niazi, LEARDI, RICCARDO
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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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Normalization in Genetic Algorithms
2003Normalization is an approach that transforms the genotype of one parent to be consistent with that of the other parent. It is a method for alleviating difficulties caused by redundant encodings in genetic algorithms. We show that normalization plays a role of reducing the search space to another one of less size.
Sung-Soon Choi, Byung Ro Moon
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