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Herbicide-Resistant Weeds
Published in Yeqiao Wang, Landscape and Land Capacity, 2020
Some weed species seem to be more prone to herbicide resistance than others. Some species have increased mutation rates or increased genetic variability. Increased variability is usually found in cross-pollinating species. Selection of resistant biotypes varies depending upon how likely it is for the resistance mutation to be lethal.
Developing mitochondrial DNA field-compatible tests
Published in Critical Reviews in Environmental Science and Technology, 2022
Bidhan C. Dhar, Christina E. Roche, Jay F. Levine
Mitochondrial DNA has a higher mutation rate than that of nuclear genetic markers, the majority of which are not harmful to their organisms (Casselman, 2017). For this reason, scientists determine mtDNA sequences to compare mutation rates and contrast mutation rates in people or other organisms. The broad biological role of mtDNA, its high copy number relative to the single copy of nuclear DNA in each cell, maternal inheritance and a high mutation rate make it a prime target for environmental monitoring and species identification in studies of free-ranging populations (Wallace, 2007). Current molecular methods implemented for mtDNA detection, however, are predominately dependent on the limited availability and location of relatively expensive laboratory equipment, which hinders its applicability for on-site field testing.
A survey on association rule mining based on evolutionary algorithms
Published in International Journal of Computers and Applications, 2021
Bodrunnessa Badhon, Mir Md. Jahangir Kabir, Shuxiang Xu, Monika Kabir
Generally Crossover (such as single point, two points, uniform, ordered crossover) and mutation operators (swapping mutation) are used. Hu et al. [14] proposed Pareto-based neighborhood crossover along with an annexing operator. In Kabir et al. [15], adaptive mutation technique was used in which rise in the generation number decreases the mutation rate while adjustment to this mutation rate is made based on offspring’s fitness value. In Fister Jr. et al. [16] uses differential mutation and a differential crossover. Differential mutation technique chooses two solution at random and adds the difference of these two random solution to the third solution which can be expressed as: Where m1, m2 and m3 are randomly chosen values in the interval 1 to . K is the scaling factor where however differential evolution (DE) community most frequently uses the value of K between the interval 0.1 and 1.0. Differential crossover technique uses uniform crossover that builds a trail vector by copying the parameter values of two different solutions that can be denoted by
Multi-objective macrogeometry optimization of gears: Comparison between sequential quadratic programing and genetic algorithm
Published in Mechanics Based Design of Structures and Machines, 2023
Ekansh Chaturvedi, Pinar Acar, Corina Sandu
The crossover and mutation steps are imperative to GA (Deb 1989; Goldberg 1987) as shown in Figure 4. These steps are necessary for maintaining the diversity of the populations to ensure that a global optimum is reached. The crossover step produced new off springs from the elite parents by swapping the design variables among the populations. In a real coded GA, the mutation is achieved by adding a random number between the bounds to the regularized variable. However, sometimes this is not sufficient for objective functions which are highly multimodal in nature. In such cases, GA converges prematurely and then again starts diverging with the succeeding generations (Li et al. 2003; Storn and Price 1997; Yin and Germay 1993; Miller and Shaw 2022; Pham and Karaboga 1997). To prevent this, two techniques were brought to use:Randomized crossover: Usually, crossover point is a fixed point. However, the diversity can be further enhanced in populations by using a random integer every time the crossover is performed.Variable mutation rates: Usually, mutation is done by adding a random number to the variable. The magnitude of mutation can be altered by using quadratic mutation rates (Miller and Shaw 2022). The equations below illustrate the difference between uniform and variable mutation rates: where g and G represent the current generation count and the maximum number of generations, respectively, is the value after mutation, is the value after crossbreeding, and is the value of the random number. The variable mutation rate used here is essentially a parabolic function that maximizes the mutation rate during the middle of the population progression toward predefined maximum no. of generations. This ensures that population diversity is maximized as the generations evolve.