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Identification of general fuzzy measures by genetic algorithms based on partial information
Summary
This study introduces a genetic algorithm for identifying general fuzzy measures, simplifying data collection through attribute subset sampling. The method effectively determines fuzzy measures even with partial information, yielding satisfactory results.
Area of Science:
- Fuzzy mathematics
- Computational intelligence
Background:
- Accurate identification of general fuzzy measures is crucial but often hindered by practical data collection challenges.
- Existing methods may require extensive datasets, limiting their applicability.
Purpose of the Study:
- To develop an efficient identification procedure for general fuzzy measures.
- To address the limitations of data collection by utilizing partial information.
Main Methods:
- A novel genetic algorithm specifically designed for fuzzy measure identification.
- A sampling procedure to simplify input data by focusing on attribute subsets.
- Development of a specialized initialization procedure, fitness function, and genetic operations.
Main Results:
- The proposed genetic algorithm successfully identifies general fuzzy measures.
- The method demonstrates effectiveness even when working with limited, partial information.
- Simulated experimental data confirmed the algorithm's applicability across typical classes.
Conclusions:
- Genetic algorithms provide a viable and effective approach for determining general fuzzy measures.
- The developed procedure offers a practical solution for scenarios with incomplete data.
- The method shows promise for real-world applications requiring fuzzy measure identification.
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