Genetic Algorithm, Quantum Genetic Algorithm, and Novel Genetic Algorithm: A Comparative Analysis

This program conducts a comparative study of three distinct evolutionary algorithms - Genetic Algorithm (GA), Quantum Genetic Algorithm (QGA), and a Novel Genetic Algorithm - using function optimization problems as test cases. The comparison clearly reveals the relationships and distinctions between these algorithms, along with their respective strengths and limitations. Through this implementation, we gain deeper insights into the working mechanisms of each algorithm, including how GA uses selection, crossover, and mutation operations; how QGA incorporates quantum computing principles like qubit representation and quantum gates; and what innovative approaches the novel algorithm introduces. The code structure allows users to evaluate which algorithm best suits specific problem domains based on convergence speed, solution quality, and computational efficiency. The program provides a framework for further research into algorithm improvements and optimizations, such as parameter tuning, hybrid approaches, and performance enhancements. Ultimately, this comparative implementation serves as a comprehensive analytical tool for better understanding and applying genetic algorithms, quantum genetic algorithms, and novel genetic algorithm variants in optimization problems.