A Genetic Algorithm with Fuzzy Crossover Operator and Probability

Varnamkhasti, M. Jalali; Lee, Lai Soon; Bakar, Mohd Rizam Abu; Leong, Wah June

doi:10.1155/2012/956498

Cited by 26 publications

(15 citation statements)

References 10 publications

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“…In single point cross over, two strings in the reproducing pool are selected at random and some portions of the strings are exchanged between the strings. The reproduction operator chooses noble strings and the crossover operator recombines noble strings together to optimistically create an improved sub-string (Varnamkhasti et al 2012; Ozcelik and Erzulumla 2006). The mutation operator varies a string locally to optimistically create a novel string.…”

Section: Methodsmentioning

confidence: 99%

Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point

Eswari

kavya

2016

AMB Expr

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Rhamnolipids are bio surfactants which are extra-cellular glycolipids composed of l-rhamnose and 3-hydroxyalkanoics. Rhamnolipids are produced through fermentation process by using Pseudomonas sp. as the species. An alteration to the traditional procedures in order to achieve increase in the production of biosurfactants, a numerous process technologies have been adopted in fed batch mode. Fed batch mode facilitates the high production of product by avoiding the substrate or product inhibition line of attack. To overcome the controlling parameters which reduce the product yield, optimal control profiles are designed. In order to develop viable control methods for fed-batch fermentation of Rhamnolipid production, multiple substrate feeding strategies were employed and their efficiencies were compared with different substrates concentration of glucose, nitrogen and phosphorous. The product formation depends upon the substrate feeding strategy and so, the fed-batch fermentation was carried out by using P. aeruginosa providing substrates at manifold rates. With the obtained experimental data, using the kinetic models (logistic equation and by Luedeking Piret), the kinetic parameters were estimated. These kinetic parameters were implemented in tabu search algorithm and this programme was executed in Dev-C++, optimal control profiles were generated as a result. These obtained optimal control profiles have shown an increase in productivity of rhamnolipid with a decline in computational time. Through this procedure, the optimal control profiles of substrate feeding strategies of glucose, nitrogen and phosphorous were estimated. In comparison with other algorithms like genetic algorithm, Tabu Search algorithm was able to generate an accurate optimal control profiles with a reduction in their intricacy.

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Section: Methodsmentioning

confidence: 99%

Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point

Eswari

kavya

2016

AMB Expr

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“…The flow of a commonly genetic algorithm is illustrated in Figure 5. Applying the fuzzy adaptation as we mentioned above the new structure of the genetic algorithm [25,26] is illustrated in Figure 6.…”

Section: Problem Descriptionmentioning

confidence: 99%

Fuzzy Dynamic Adaptation of Gap Generation and Mutation in Genetic Optimization of Type 2 Fuzzy Controllers

Cervantes

Castillo

Hidalgo

et al. 2018

Advances in Operations Research

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We propose to use an approach based on fuzzy logic for the adaptation of gap generation and mutation probability in a genetic algorithm. The performance of this method is presented with the benchmark problem of flight control and results show how it can decrease the error during the flight of an airplane using fuzzy logic for some parameters of the genetic algorithm. In this case of study, we use fuzzy systems for adapting two parameters of the genetic algorithm to improve the design of a type 2 fuzzy controller and enhance its performance to achieve flight control. Finally, a statistical test is presented to prove the performance enhancement in the application using fuzzy adaptation in the genetic algorithm. It is important to mention that not only is this idea for control problems but also it can be used in pattern recognition and many different problems.

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“…Poor selection can lead to premature convergence due to reduced diversity in the population over several iterations [18]. While the mutation operator is usually responsible for the maintenance of diversity, an extremely high level of mutation at the beginning can impede convergence on the solution.…”

Section: Adaptable Genetic Algorithmmentioning

confidence: 99%

Fuzzy-Logic Controlled Genetic Algorithm for the Rail-Freight Crew-Scheduling Problem

et al. 2017

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A Genetic Algorithm with Fuzzy Crossover Operator and Probability

Cited by 26 publications

References 10 publications

Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point

Optimal feed profile for the Rhamnolipid kinetic models by using Tabu search: metabolic view point

Fuzzy Dynamic Adaptation of Gap Generation and Mutation in Genetic Optimization of Type 2 Fuzzy Controllers

Fuzzy-Logic Controlled Genetic Algorithm for the Rail-Freight Crew-Scheduling Problem

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