Beginner's Guide to Genetic Algorithm Implementation

This is a basic implementation program intended to assist beginners in understanding genetic algorithms. The code comprehensively demonstrates most key steps in genetic algorithm implementation and includes detailed inline comments explaining each operation. Additionally, I hope this program can stimulate further discussion and improvements. Key implementation aspects covered: - Population initialization with random chromosome generation - Fitness function calculation and evaluation - Selection mechanisms (e.g., roulette wheel or tournament selection) - Crossover operations with configurable parameters - Mutation operations with probability controls Performance observation: During large-scale computations, genetic algorithms involve significant data exchange, making speed a notable bottleneck. As demonstrated by this program, with a population size of 50 and 1000 evolution generations, the runtime reaches approximately 40 seconds (CPU: Duron 1G). I acknowledge limitations in my programming skills and welcome suggestions for optimization or code modifications from experienced developers to enhance computational efficiency. The program structure follows standard genetic algorithm workflow: 1. Initialize population with random individuals 2. While termination condition not met: - Evaluate fitness for each individual - Select parents based on fitness - Apply crossover to create offspring - Apply mutation to introduce diversity - Replace population with new generation