Genetic Algorithms

Sastry, Kumara; Goldberg, David E.; Kendall, Graham

doi:10.1007/0-387-28356-0_4

Cited by 237 publications

(182 citation statements)

References 52 publications

Supporting

Mentioning

164

Contrasting

Unclassified

Order By: Relevance

“…GA is a meta-heuristic approach that attempt to simulate the natural evolution process and optimize the desired function using some natural evolutional operators such as mutation, crossover and selection (Haupt and Haupt, 2004;Sastry et al, 2005). In GA, a population of candidate solutions that called chromosomes is initialized with random values.…”

Section: Optimization Of the Rfm By Gamentioning

confidence: 99%

“…In order to overcome this problem, optimization based algorithms seems to be an appropriate solution. Genetic Algorithm (Haupt and Haupt, 2004;Sastry et al, 2005) Shi and Eberhart, 1998) as two important evolutionary algorithms are frequently used by the researchers to find the optimal solutions. In this paper, the possibility of using GA and PSO are investigated to find the optimal combination of coefficients which may lead to eliminate over-parameterization errors, to reduce GCPs and to rectify HRSIs with more accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Particle Swarm Optimization and Genetic Algorithm in Rational Function Model Optimization

Yavari

Zoej²,

Mokhtarzade³

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Rational Function Models (RFM) are one of the most considerable approaches for spatial information extraction from satellite images especially where there is no access to the sensor parameters. As there is no physical meaning for the terms of RFM, in the conventional solution all the terms are involved in the computational process which causes over-parameterization errors. Thus in this paper, advanced optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are investigated to determine the optimal terms of RFM. As the optimization would reduce the number of required RFM terms, the possibility of using fewer numbers of Ground Control Points (GCPs) in the solution comparing to the conventional method is inspected. The results proved that both GA and PSO are able to determine the optimal terms of RFM to achieve rather the same accuracy. However, PSO shows to be more effective from computational time part of view. The other important achievement is that the algorithms are able to solve the RFM using less GCPs with higher accuracy in comparison to conventional RFM.

show abstract

Section: Optimization Of the Rfm By Gamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Particle Swarm Optimization and Genetic Algorithm in Rational Function Model Optimization

Yavari

Zoej²,

Mokhtarzade³

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…GAlib [23] is a recommendation by [14]. In our work, we utilize a minimized version of GAlib called GAlite [24].…”

Section: Methodsmentioning

confidence: 99%

“…It is recommended to initiate GA implementation using existing GA library to save implementation time [14]. GAlib [23] is a recommendation by [14].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Genetic Algorithm Optimized Packet Filtering

Chen¹,

Zhang²,

Hao³

et al. 2013

IJCA

View full text Add to dashboard Cite

Hyper‐Heuristics

Kendall¹,

Burke²

2011

Wiley Encyclopedia of Operations Research and Management Science

View full text Add to dashboard Cite

This article provides an introduction to hyperheuristics, the origins of which can be traced back to the 1960s. There was a significant increase in research interest from about 2000. Hyperheuristics can be seen as either heuristics to choose heuristics or as heuristics to generate heuristics. In contrast to most uses of metaheuristics, which usually search the solution space directly, hyperheuristics search over a heuristic space. One motivation of the area is that, by operating in this way it might be possible to raise the level of generality of search algorithms so that they can address a broader spectrum of problems (without human adaptation) than is possible today. Another long‐term aim of hyperheuristic research is to build search methodologies that are able to automatically adapt themselves to different problem instances/domains with as little human input as possible. This article provides access to relevant literature, in addition to providing a brief history of the area and describing the two approaches (selection of heuristics and generating heuristics) mentioned above. It is intended as a brief and initial introduction to the area.

show abstract

Genetic Algorithms

Cited by 237 publications

References 52 publications

Comparison of Particle Swarm Optimization and Genetic Algorithm in Rational Function Model Optimization

Comparison of Particle Swarm Optimization and Genetic Algorithm in Rational Function Model Optimization

Genetic Algorithm Optimized Packet Filtering

Hyper‐Heuristics

Contact Info

Product

Resources

About