A Multiple Mitosis Genetic Algorithm

Kamil, Karmila Hanim; Chong, K. H.; Hashim, Huzaifa; Shaaya, Sharifah Azwa

doi:10.11591/ijai.v8.i3.pp252-258

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…There are many parents who are working to produce a new generation. GA is a research and optimization technique whose objective is to identify appropriate parameters by which results can be improved [40], [41]. Their candidate solutions are called chromosomes [12], [23], [42], [43].…”

Section: Methodsmentioning

confidence: 99%

Studies enhancement of transient stability by single machine infinite bus system and setting purpose genetic algorithm

Hasan

Al-Mamoori

2022

IJEECS

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Maintaining network synchronization is important to customer service. Low fluctuations cause voltage instability, non-synchronization in the power system or the problems in the electrical system disturbances, harmonics current and voltages inflation and contraction voltage. Proper tunning of the parameters of stabilizer is prime for validation of stabilizer. To overcome instability issues and get reinforcement found a lot of the techniques are developed to overcome instability problems and improve performance of power system. Genetic algorithm was applied to optimize parameters and suppress oscillation. The simulation of the robust composite capacitance system of an infinite single-machine bus was studied using MATLAB was used for optimization purpose. The critical time is an indication of the maximum possible time during which the error can pass in the system to obtain stability through the simulation. The effectiveness improvement has been shown in the system.

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Section: Methodsmentioning

confidence: 99%

Studies enhancement of transient stability by single machine infinite bus system and setting purpose genetic algorithm

Hasan

Al-Mamoori

2022

IJEECS

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“…This section discusses how meta-heuristic algorithms [29], [67]- [70], [73]- [75], [82], [90], [95], [109], [119], [160], [163], [176], [179]- [188] are included in the fuzzy modelling process. The implementation of fuzzy meta-heuristic algorithms is depicted in Figure 6.…”

Section: Fuzzy Meta-heuristic Algorithmsmentioning

confidence: 99%

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

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The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.

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“…Various researchers have forged new selection operators for different problems like researcher Kaya suggests a new operator [26] and studies its effects on the production cost of beams. However, different variants of genetic algorithms have been suggested by various researchers, some mix other heuristic algorithms to create a hybrid algorithm GA [27], while others have modified some operations within the GA [28].…”

Section: Figure 1 Pseudo Code For Genetic Algorithmmentioning

confidence: 99%

Analysis and comparison of a proposed mutation operator and its effects on the performance of genetic algorithm

Ullah

Salam²,

Masood³

2022

IJEECS

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Genetic algorithms (GAs) are dependent on various operators and parameters. The most common evolutionary operators are parent selection, crossover, and mutation. Each operator has broad implementations with its pros and cons. A successful GA is highly dependent on genetic diversity which is the main driving force that steers a GA towards an optimal solution. Mutation operator implements the idea of exploration to search for uncharted areas and introduces diversity in a population. Thus, increasing the probability of GA to converge to a globally optimum solution. In this paper, a new variant of mutation operator is proposed, and its functions are studied and compared with the existing operators. The proposed mutation operator as well as others such as m-mutation, shuffle, swap, and inverse are tested for their ability to introduce diversity in population and hence, their effects on the performance of GA. All these operators are applied to Max one problem. The results concluded that the proposed variant is far more superior to the existing operators in terms of introducing diversity and hence early convergence to an optimum solution.

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A Multiple Mitosis Genetic Algorithm

Cited by 9 publications

References 9 publications

Studies enhancement of transient stability by single machine infinite bus system and setting purpose genetic algorithm

Studies enhancement of transient stability by single machine infinite bus system and setting purpose genetic algorithm

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Analysis and comparison of a proposed mutation operator and its effects on the performance of genetic algorithm

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