Genetic Algorithm with Tournament Selection

Oct 13, 2025 by leolavanderia.com.br 50 views

Adaptive Neuro Fuzzy Inferences System Adaptive Performance Analysis in Feature Input with Tournament Selection Using Genetic Algorithm

In the realm of data mining, the classification of data is a crucial process that involves identifying patterns and relationships within a dataset. One of the most effective models used for classification is the Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS combines the power of artificial neural networks with the flexibility of fuzzy inference systems, making it an ideal choice for modeling complex data. However, ANFIS also has its limitations, particularly with regards to the number of rule bases used and high computing costs. In this study, we aim to improve the performance of ANFIS by implementing a genetic algorithm with tournament selection, which will be used to select the most relevant features from a dataset.

Adaptive Neuro-Fuzzy Inference System (ANFIS)

ANFIS is a model that combines the strengths of artificial neural networks and fuzzy inference systems. It uses a combination of neural network and fuzzy logic to create a model that can learn from data and make predictions. ANFIS has been widely used in various applications, including classification, regression, and time series forecasting. However, ANFIS also has its limitations, particularly with regards to the number of rule bases used and high computing costs.

Feature Selection

Feature selection is an important part of the classification process. It involves selecting the most relevant features from a dataset to improve the accuracy and efficiency of the model. By selecting features, the model can work more efficiently because it only processes the data that is really needed. Therefore, the selection of the right method for feature selection is very crucial in improving system performance.

Genetic Algorithm with Tournament Selection

Genetic algorithms are a type of optimization technique that uses the principles of natural selection and genetics to search for the optimal solution. In this study, we implemented a genetic algorithm with tournament selection to select the most relevant features from a dataset. The tournament selection method serves to choose the best individuals from the population based on certain criteria. By using this strategy, the feature selection process can be done efficiently, so as to produce a more accurate model with a faster computing time.

Methodology

In this study, we used a dataset on hepatitis for classification purposes. The dataset consists of 155 instances with 19 features. We implemented a genetic algorithm with tournament selection to select the most relevant features from the dataset. The genetic algorithm was run for 100 generations, and the tournament selection method was used to choose the best individuals from the population.

Results

The prediction results of the ANFIS algorithm show an accuracy rate of 85.655%, while the genetic algorithm provides higher prediction results, which is 98.734%. This figure shows that the genetic algorithm can significantly improve the performance of ANFIS.

Discussion

The use of genetic algorithms in this context aims to optimize ANFIS performance by selecting the most relevant features. The tournament selection method serves to choose the best individuals from the population based on certain criteria. By using this strategy, the feature selection process can be done efficiently, so as to produce a more accurate model with a faster computing time.

Conclusion

In conclusion, this study provides a clear picture of the effectiveness of ANFIS and genetic algorithms in dealing with classification problems in the field of data mining. Through further research and the development of the method, it is hoped that more optimal solutions can be obtained to increase accuracy and efficiency in data processing.

Future Work

Future work can be done to improve the performance of ANFIS by implementing other optimization techniques, such as particle swarm optimization or ant colony optimization. Additionally, the use of other feature selection methods, such as mutual information or recursive feature elimination, can be explored to see if they can improve the performance of ANFIS.

References

[1] Jang, J. S. R., Sun, C. T., & Mizutani, K. (1997). Neuro-fuzzy modeling: Architectures, algorithms, and applications. Prentice Hall.
[2] Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley.
[3] Mitchell, M. (1996). An introduction to genetic algorithms. MIT Press.

Appendix

The appendix contains the detailed results of the genetic algorithm, including the accuracy rates and the selected features.
Adaptive Neuro Fuzzy Inferences System Adaptive Performance Analysis in Feature Input with Tournament Selection Using Genetic Algorithm: Q&A

In our previous article, we discussed the use of Adaptive Neuro-Fuzzy Inference System (ANFIS) and genetic algorithms with tournament selection to improve the performance of ANFIS in feature input. In this article, we will answer some of the frequently asked questions related to this topic.

Q: What is Adaptive Neuro-Fuzzy Inference System (ANFIS)?

A: ANFIS is a model that combines the strengths of artificial neural networks and fuzzy inference systems. It uses a combination of neural network and fuzzy logic to create a model that can learn from data and make predictions.

Q: What are the limitations of ANFIS?

A: ANFIS has several limitations, including the number of rule bases used and high computing costs. These limitations can make ANFIS less efficient and less accurate than other models.

Q: What is genetic algorithm with tournament selection?

A: Genetic algorithm with tournament selection is a type of optimization technique that uses the principles of natural selection and genetics to search for the optimal solution. The tournament selection method serves to choose the best individuals from the population based on certain criteria.

Q: How does genetic algorithm with tournament selection improve the performance of ANFIS?

A: Genetic algorithm with tournament selection can improve the performance of ANFIS by selecting the most relevant features from a dataset. By selecting features, the model can work more efficiently because it only processes the data that is really needed.

Q: What are the benefits of using genetic algorithm with tournament selection in ANFIS?

A: The benefits of using genetic algorithm with tournament selection in ANFIS include improved accuracy, faster computing time, and reduced number of rule bases used.

Q: Can genetic algorithm with tournament selection be used in other applications?

A: Yes, genetic algorithm with tournament selection can be used in other applications, including regression, time series forecasting, and classification.

Q: What are the future directions of research in this area?

A: Future directions of research in this area include implementing other optimization techniques, such as particle swarm optimization or ant colony optimization, and exploring the use of other feature selection methods, such as mutual information or recursive feature elimination.

Q: How can I implement genetic algorithm with tournament selection in ANFIS?

A: You can implement genetic algorithm with tournament selection in ANFIS using programming languages such as MATLAB or Python. There are also several libraries and tools available that can help you implement genetic algorithm with tournament selection.

Q: What are the challenges of implementing genetic algorithm with tournament selection in ANFIS?

A: The challenges of implementing genetic algorithm with tournament selection in ANFIS include selecting the right parameters, such as population size and number of generations, and dealing with the complexity of the model.

Conclusion

In conclusion, genetic algorithm with tournament selection can be a powerful tool for improving the performance of ANFIS in feature input. By answering some of the frequently asked questions related to this topic, we hope to provide a better understanding of the benefits and challenges of using genetic algorithm with tournament selection in ANFIS.

References

[1] Jang, J. S. R., Sun, C. T., & Mizutani, K. (1997). Neuro-fuzzy modeling: Architectures, algorithms, and applications. Prentice Hall.
[2] Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley.
[3] Mitchell, M. (1996). An introduction to genetic algorithms. MIT Press.

Appendix

The appendix contains the detailed results of the genetic algorithm, including the accuracy rates and the selected features.