Results 231 to 240 of about 451,148 (267)

Genetic algorithms and their applications

IEEE Signal Processing Magazine, 1996
This 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, Kim-Fung Man, Sam Kwong, Qianhua He   +3 more
openaire   +1 more source

Genetic algorithm portfolios

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 ...
openaire   +1 more source

Homogeneous genetic algorithms

Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2007
A 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 ...
openaire   +1 more source

An introduction to genetic algorithms

Sadhana, 1999
Genetic 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.
openaire   +2 more sources

Genetic clustering algorithms

European Journal of Operational Research, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu-Chiun Chiou, Lawrence W. Lan
openaire   +1 more source

Genetic algorithms and evolution

Journal of Theoretical Biology, 1990
The 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, A I, Houston, J M, McNamara, W D, Hamilton   +3 more
openaire   +2 more sources

The compact genetic algorithm

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, Fernando G. Lobo, David E. Goldberg   +2 more
openaire   +1 more source

On genetic algorithms

Proceedings of the eighth annual conference on Computational learning theory - COLT '95, 1995
Eric B. Baum, Dan Boneh, Charles Garrett
openaire   +1 more source

Normalization in Genetic Algorithms

2003
Normalization 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
openaire   +1 more source

Genetic testing in prostate cancer management: Considerations informing primary care

Ca-A Cancer Journal for Clinicians, 2022
Mary-Ellen Taplin, Veda Giri, Jacob Berchuck   +2 more
exaly