caa a22 4500 46794590X CHVBK 20180323165700.0 cr unu---uuuuu 170328e19881001xx s 000 0 eng 10.1007/BF00113893 doi (NATIONALLICENCE)springer-10.1007/BF00113893 (NATIONALLICENCE)springer-10.1023/A:1022654003254 Genetic algorithms in noisy environments [Elektronische Daten] [J. Fitzpatrick, John Grefenstette] Genetic algorithms are adaptive search techniques which have been used to learn high-performance knowledge structures in reactive environments that provide information in the form of payoff. In general, payoff can be viewed as a noisy function of the structure being evaluated, and the learning task can be viewed as an optimization problem in a noisy environment. Previous studies have shown that genetic algorithms can perform effectively in the presence of noise. This work explores in detail the tradeoffs between the amount of effort spent on evaluating each structure and the number of structures evaluated during a given iteration of the genetic algorithm. Theoretical analysis shows that, in some cases, more efficient search results from less accurate evaluations. Further evidence is provided by a case study in which genetic algorithms are used to obtain good registrations of digital images. Kluwer Academic Publishers, 1988 Learning in noisy environments nationallicence genetic algorithms nationallicence image registration nationallicence Fitzpatrick J. Computer Science Department, Vanderbilt University, 37235, Nashville, Tennessee aut Grefenstette John Computer Science Department, Vanderbilt University, 37235, Nashville, Tennessee aut Machine Learning Kluwer Academic Publishers 3/2-3(1988-10-01), 101-120 0885-6125 3:2-3<101 1988 3 10994 https://doi.org/10.1007/BF00113893 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00113893 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fitzpatrick J. Computer Science Department, Vanderbilt University, 37235, Nashville, Tennessee aut NATIONALLICENCE 700 1- Grefenstette John Computer Science Department, Vanderbilt University, 37235, Nashville, Tennessee aut NATIONALLICENCE 773 0- Machine Learning Kluwer Academic Publishers 3/2-3(1988-10-01), 101-120 0885-6125 3:2-3<101 1988 3 10994 NATIONALLICENCE 856 40 https://doi.org/10.1023/A:1022654003254 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fitzpatrick J. Computer Science Department, Vanderbilt University, 37235., Nashville, Tennessee aut NATIONALLICENCE 700 1- Grefenstette John Computer Science Department, Vanderbilt University, 37235., Nashville, Tennessee aut NATIONALLICENCE 773 0- Machine Learning Kluwer Academic Publishers-Plenum Publishers 3/2-3(1988-10-01), 101-120 0885-6125 3:2-3<101 1988 3 10994 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE NATIONALLICENCE NL-springer