Solving Function Optimization Using Genetic Algorithms

This implementation utilizes genetic algorithms to find optimal solutions for functions, characterized by rapid convergence and reduced susceptibility to local optima. As a classical optimization approach, the program is particularly suitable for beginners, providing foundational understanding of evolutionary computation techniques. The algorithm's core components include:

• Population initialization with random chromosome generation
• Fitness function evaluation using objective function values
• Roulette wheel or tournament selection for parent chromosome selection
• Single-point or multi-point crossover operations for offspring generation
• Mutation operators with configurable probability rates

Beyond function optimization, genetic algorithms can be extended to solve complex problems like Traveling Salesman Problem (TSP) and logistics optimization. Algorithm performance can be enhanced through parameter tuning (population size, mutation rate) and strategy adjustments (elitism, adaptive operators). Employing genetic algorithms for function optimization presents opportunities for algorithmic exploration while improving solution accuracy and stability through iterative refinement.