Variable coded hierarchical fuzzy classification model using DNA coding and evolutionary programming

Feng, Ting Cheng; Li, Tzuu-Hseng S.; Kuo, Ping‐Huan

doi:10.1016/j.apm.2015.03.004

Cited by 20 publications

(10 citation statements)

References 65 publications

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“…Therefore, if the number transferred from the DNA nucleotide according to Table 1 is less than or equal to 32, then the peak of the membership function shifts proportionally to the left as in Equation (11). In the same way, the peak of the membership function shifts to the right if the number is greater than 32.…”

Section: Dna Computingmentioning

confidence: 92%

“…The corresponding number of each codons is used in DNA coding, and it is defined as number 0 to number 63, which is from the top left block to the bottom right block. Inspired by Jan [11], who utilized DNA coding to construct structures of P, PD, PI and PID in a PID controller, the crossover, mutation, enzyme, and virus operations is applied to obtain the fittest structure. The chromosome structure used in this study is changed dynamically by defining each chromosome as two partitions, one is the membership function and the other is related to the shift nucleotide as shown in Figure 3.…”

Section: Dna Computingmentioning

confidence: 99%

“…It was found that DNA coding produces a larger, more diversified population compared to regular coding. Feng [11] enhance the membership function construction by using DNA computing. DNA computing includes the operations of transformation, translocation, enzyme, and virus [12], which resemble those operations of GA and reinforce it.…”

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confidence: 99%

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Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Feng¹,

Li²

2016

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This paper offers a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. Moreover, from the best of our knowledge, previous research on the ABC algorithm has not combined it with DNA computing for hierarchical fuzzy classification to explore the merits of cooperative coevolution. Therefore, this paper is the first to apply the mechanism of symbiosis to create a hybrid modified DNA-ABC algorithm for hierarchical fuzzy classification applications. In this study, the partition number and the shape of the membership function are extracted by the symbiosis based hybrid modified DNA-ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. The proposed optimization algorithm is applied on five benchmark University of Irvine (UCI) data sets, and the results prove the efficiency of the algorithm.

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Section: Dna Computingmentioning

confidence: 92%

Section: Dna Computingmentioning

confidence: 99%

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Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Feng¹,

Li²

2016

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“…In recent years, fuzzy systems have been successfully used in various fields of science [1], engineering [2] and medicine [3] for various applications such as control, modeling and classification [4]. Fuzzy rules have become very popular with users due to their high readability, easy interpretation by humans, and the provision of insights into the knowledge embedded in classification systems [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Structure of Highly Interpretable Fuzzy Rules Extraction

Ahouz¹,

Golabpour

2021

Front Health Inform

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Introduction: Extracting effective rules from medical data with two indicators of accuracy and high interpretability is essential to increase the accuracy and speed of diagnosis by specialists. As a result, the production of medical assistant systems that are able to detect the rules governing the data plays a vital role in early detection of the disease and thus increase the chances of treatment, disease control and maintaining the quality of life of patients.Material and Methods: In this paper, a system of automatic extraction of rules from medical data by a new hybrid method based on fuzzy logic and genetic algorithm is presented. Genetic algorithms are used to automatically generate these rules. The Parkinson UCI dataset including 195 records and 23 variables was used to evaluate the proposed method based on the criteria of interpretability, accuracy, sensitivity and specificity.Results: The evaluation of the proposed model on the Parkinson's dataset was the accuracy of 84.62%. This accuracy is supported by 4 fuzzy rules with an average rule length of 2 and using 7 linguistic terms extremely low, very low, low, normal, high, very high and extremely high. All fuzzy membership functions that represent each term have the same width.Conclusion: The proposed method, based on the three criteria of low number of rules, short rule length and symmetric membership functions with equal width for all variables, is quite suitable for automatic production of accurate and compact rules with high interpretability in medical data. . A 90% dimensionality reduction in the experimental evaluation showed that this model could be used to implement real-time systems.

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“…These algorithms extend conventional genetic algorithms [18] and DNA computing [19], both of which have been widely used in solving complex problems [20], [21] by mimicking the genetic mechanisms in the nature. Existing DNA-GAs encode individuals using single DNA strands [22], therefore, still have the same issues encountered by existing MOEAs, as discussed earlier.…”

Section: Introductionmentioning

confidence: 99%

A Novel Double-Strand DNA Genetic Algorithm for Multi-Objective Optimization

et al. 2019

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Multi-objective optimization is important for many businesses, science, and engineering applications. Existing evolutionary algorithms for multi-objective optimization problems based on single chain encoding still have difficulties in obtaining high-quality results. This paper presents a new DNA genetic algorithm that uses a novel double-strand DNA encoding, a set of new genetic operators, and two new ranking criteria to obtain solutions that closely approximate the Pareto-optimal front. The extensive experiments were performed using a set of comprehensive benchmark bi-objective and tri-objective test problems. The experimental results show that this algorithm outperforms a set of the state-of-the-art evolutionary algorithms on several well-accepted performance metrics. INDEX TERMS Multi-objective optimization, DNA genetic algorithm, variant crowding distance, double strands, non-dominated sorting.

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Variable coded hierarchical fuzzy classification model using DNA coding and evolutionary programming

Cited by 20 publications

References 65 publications

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

Advanced Hierarchical Fuzzy Classification Model Adopting Symbiosis Based DNA-ABC Optimization Algorithm

A Novel Structure of Highly Interpretable Fuzzy Rules Extraction

A Novel Double-Strand DNA Genetic Algorithm for Multi-Objective Optimization

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