Finding Function Minimum Using Genetic Algorithm

The following example illustrates how to find the minimum value of a function using a genetic algorithm. Genetic algorithms are optimization techniques inspired by natural evolution processes, employing operations such as selection, crossover, and mutation to generate and refine candidate solutions. In implementation, the algorithm typically initializes a population of random solutions, evaluates their fitness using the target function, and iteratively applies genetic operators to produce improved generations. Key components include fitness proportionate selection (e.g., roulette wheel selection), crossover methods (like single-point or uniform crossover) for combining parent solutions, and mutation operators to maintain diversity. Through successive generations, the algorithm converges toward optimal solutions. This example explores parameter tuning (population size, mutation rate) and optimization strategies for efficient convergence. The code structure would involve initializing population vectors, implementing fitness evaluation functions, and designing termination criteria (e.g., maximum generations or fitness threshold).