Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural...

Area of Science:

• Artificial Intelligence
• Tourism Economics
• Computational Intelligence

Background:

• Rapid growth in China's tourism economy necessitates advanced evaluation methods.
• Neural network technology offers potential for complex data analysis in resource management.

Purpose of the Study:

• To develop and evaluate a neural network comprehensive evaluation model for tourism resources.
• To design a system for analyzing factors influencing tourism resource development using a specific algorithm.

Main Methods:

• Utilized a neural network comprehensive evaluation model.
• Employed a multispecies evolutionary genetic algorithm for analysis and evaluation.
• Collected data on resource income, tourism investment, and sustainability.

Main Results:

• The model demonstrated high practicability and effective data integration.
• Achieved good sorting of variable ratios for influencing factors.
• Enabled accurate analysis and comparison of diverse tourism resource characteristics.

Conclusions:

• The developed model provides an effective tool for comprehensive tourism resource evaluation.
• The multispecies evolutionary genetic algorithm enhances the analysis of complex influencing factors.
• The system supports informed decision-making in tourism development and sustainability.