Feature description

This feature would introduce a new genetic algorithm-based approach to optimize mathematical functions. It would allow users to find the minimum or maximum values of continuous functions using genetic algorithms.

Key Components:

• Target Function: The user can define their own function to optimize, for example, ( f(x, y) = x^2 + y^2 ), or more complex functions.
• Population Initialization: Randomly generate initial solutions (chromosomes) within a defined search space.
• Fitness Function: Evaluate each chromosome's fitness based on how close the function value is to the desired optimum (minimization or maximization).
• Selection: Use selection methods like tournament selection or roulette wheel to pick parents for crossover based on their fitness scores.
• Crossover: Implement crossover strategies such as one-point, two-point, or uniform crossover to combine parent chromosomes into offspring.
• Mutation: Introduce random mutations to offspring to maintain diversity in the population and avoid local minima.
• Termination Condition: Allow the algorithm to stop after a set number of generations or when the improvement between generations is minimal.

This implementation would be useful for users needing a flexible and easy-to-use method for solving optimization problems in continuous spaces.