Years of Genetic Algorithm Research: Optimization Code Implementation

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Resource Overview

Shared genetic algorithm code developed through years of research! Implements optimization algorithms with fitness functions, selection methods, crossover operations, and mutation mechanisms for solving complex problems.

Detailed Documentation

In this article, I will share genetic algorithm code that I have developed through years of research. This code implements optimization algorithms that help us find optimal solutions to various problems. Genetic algorithms are optimization methods based on natural selection and genetic mechanisms, using evolutionary process simulation to search for optimal solutions. I will provide detailed explanations of the code's working principles and usage methods, including key components like population initialization, fitness evaluation, tournament selection, crossover operations (such as single-point or uniform crossover), and mutation techniques. The content will also cover application cases across different domains and problem types, demonstrating how to configure parameters like population size, mutation rate, and termination conditions. I hope this code will be valuable for your research and practical applications!

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