A Multi-parent Crossover Based Genetic Algorithm for Bi-Objective Unconstrained Binary Quadratic Programming Problem

Huo, Chao; Zeng, Rong-Qiang; Wang, Yan; Shang, Mingsheng

doi:10.1007/978-981-10-3614-9_2

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Evolutionary algorithm essentially is a kind of probability of random search, but with adaptive, it refers to the "survival of the fittest" evolutionary ideas. The algorithm gives a fitness value of each individual in the population, which represents the survival probability of the individual in the process of biological evolution [25]. The higher the fitness of the individual is, the higher the probability of its entry into the next generation of reproduction is.…”

Section: Evolutionary Algorithmmentioning

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: Evolutionary Algorithmmentioning

confidence: 99%