Improved Genetic Algorithm Based on Information Entropy

In this article, we present an improved genetic algorithm based on information entropy, integrated with population concentration-based update operators. This algorithm leverages entropy principles to enhance problem-solving capabilities and deliver more accurate results. By implementing population concentration update operators, we introduce mechanisms to increase population diversity through concentration threshold checks and dynamic crossover/mutation rate adjustments. The implementation typically involves calculating population entropy to measure diversity and concentration-based operators to prevent premature convergence. This enhanced genetic algorithm shows significant potential for practical applications across various domains, including optimization problems and machine learning. Through detailed algorithm description and operator implementation examples, we aim to provide readers with comprehensive understanding of this approach and stimulate interest in its potential applications.