Ant Colony Optimization with Different Crossover Schemes for Continuous Optimization

Chen, Zhiqiang; Jiang, Yun; Wang, Ronglong

doi:10.1007/978-3-662-49014-3_5

Cited by 1 publication

(1 citation statement)

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“…Crossover [30] is the process of generating new individuals. Through the structural exchange of the individuals, the new individuals are different from the fathers, which ensure the diversity of the population in the process of evolution.…”

Section: Single Point Crossovermentioning

confidence: 99%

The Research of Disease Spots Extraction Based on Evolutionary Algorithm

Xiong

Zhang

et al. 2017

Journal of Optimization

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According to the characteristics of maize disease spot performance in the image, this paper designs two-histogram segmentation method based on evolutionary algorithm, which combined with the analysis of image of maize diseases and insect pests, with full consideration of color and texture characteristic of the lesion of pests and diseases, the chroma and gray image, composed of two tuples to build a two-dimensional histogram, solves the problem of one-dimensional histograms that cannot be clearly divided into target and background bimodal distribution and improved the traditional two-dimensional histogram application in pest damage lesion extraction. The chromosome coding suitable for the characteristics of lesion image is designed based on second segmentation of the genetic algorithm Otsu. Determining initial population with analysis results of lesion image, parallel selection, optimal preservation strategy, and adaptive mutation operator are used to improve the search efficiency. Finally, by setting the fluctuation threshold, we continue to search for the best threshold in the range of fluctuations for implementation of global search and local search.

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Section: Single Point Crossovermentioning

confidence: 99%