Original Code for Solving Function Optimization Problems Using Genetic Algorithm
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This original code implements genetic algorithm methodology to solve function optimization problems, capable of finding optimal solutions while tracking iteration counts. The genetic algorithm mimics biological evolution processes, employing operations such as natural selection, crossover, and mutation to search for optimal solutions. In this implementation, the code features population initialization with random chromosome generation, fitness evaluation using objective function calculation, tournament selection for parent choosing, single-point crossover for offspring creation, and Gaussian mutation for maintaining diversity. The algorithm terminates when convergence criteria are met or maximum iterations reached, outputting both the optimal solution value and the required iteration count for performance analysis and algorithmic improvements.
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