# Genetic Algorithm-Optimized Neural Networks: An Intelligent Solution for Tax Revenue Prediction

> This article introduces a project that combines genetic algorithms and neural networks to predict tax revenue by automatically searching for optimal network architectures, providing a practical example for machine learning modeling on small-sample nonlinear data.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-08T14:13:06.000Z
- 最近活动: 2026-06-08T14:29:18.173Z
- 热度: 154.7
- 关键词: 遗传算法, 神经网络, 税收预测, 神经架构搜索, AutoML, PyTorch, 宏观经济, 机器学习, 小样本学习, 回归预测
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-aman-k-mishra-tax-revenue-prediction-ga-nn
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-aman-k-mishra-tax-revenue-prediction-ga-nn
- Markdown 来源: floors_fallback

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## Genetic Algorithm-Optimized Neural Networks: An Intelligent Solution for Tax Revenue Prediction (Introduction)

The open-source project introduced in this article was published by Aman-K-Mishra on GitHub (project name: Tax-Revenue-Prediction-GA-NN). Its core is combining genetic algorithms (GA) with neural networks to automatically search for optimal network architectures, solving the problem of tax revenue prediction based on macroeconomic indicators—especially suitable for small-sample nonlinear data scenarios, providing a practical example for machine learning modeling.

## Problem Background: Challenges of Small-Sample Nonlinear Tax Prediction

Tax revenue prediction is an important foundation for fiscal planning and policy formulation. Traditional econometric methods assume linear relationships, but the relationships between tax revenue and factors like GDP, inflation rate, population, import-export trade, and corporate tax rates are complex and nonlinear. Although neural networks can capture nonlinearity, they face small-sample challenges: only 129 annual observation samples and 6 macroeconomic features—complex architectures are prone to overfitting, requiring intelligent methods to automatically select optimal architectures.

## Core Solution: Genetic Algorithm-Driven Neural Architecture Search

Reasons for choosing genetic algorithms: suitable for scenarios with large search spaces and discrete parameter optimization. The workflow includes: 1. Feature standardization using StandardScaler; 2. GA evolution of candidate architectures; 3. Fast evaluation using validation set MSE; 4. Retraining of the best architecture; 5. Saving the model and scaler; 6. CLI prediction interface. The input features are 6 macroeconomic indicators: GDP, inflation rate, population, import-export, and corporate tax rate.

## Model Performance: Validation Results and Evaluation Metrics

After GA search optimization, the final architecture is a feedforward neural network with two hidden layers. The performance metrics are as follows: R²=0.827, RMSE=69.5k, MAE=55.4k, MSE=4.8 billion—indicating that the model captures the relationships between variables well and reaches a practical prediction level.

## Technical Implementation: Project Structure and Core Components

The project structure includes folders like data/, models/ (for saving models and scalers), and files like predict.py, train.py, ga_search.py, etc. Core components: ga_search.py implements GA search logic, train.py handles the training process, predict.py provides a CLI prediction interface. Tech stack: Python, PyTorch, NumPy, Pandas, scikit-learn.

## Application Value: Automated Design and Fiscal Domain Applications

Value of automated architecture design: reduces manual intervention, avoids local optima, adapts to small data. Fiscal domain applications: budget planning, policy simulation, risk early warning. Educational and research value: demonstrates the combination of evolutionary algorithms and deep learning, provides a complete workflow, suitable for machine learning practice projects.

## Limitations and Future Improvement Directions

Current limitations: small dataset size, only annual data used, limited feature dimensions. Future improvement directions: use real government data, add cross-validation, try LSTM/Transformer time-series prediction, build a web dashboard, support CSV batch prediction.