The algorithm creates an initial population of random binary sequences and evolves these sequences over several generations using selection, crossover, and mutation to find a sequence with the highest number of "1s".
The Genetic Algorithm proceeds as follows:
1.It creates a population of random binary sequences.
2.It evaluates the fitness of each individual in the population (the number of "1s" in the sequence).
3.It sorts the population in descending order based on fitness, and the best individual (highest fitness) is selected and printed at each generation.
4.The new population is created by selecting parents from the top 50 individuals, and with a certain probability (CROSSOVER_RATE), crossover (recombination) is performed to produce two offspring.
5.The offspring then undergo mutation (flipping bits) with a certain probability (MUTATION_RATE).
6.The new population, including the best individual from the previous generation, becomes the population for the next generation, and the process continues for a specified number of generations (NUM_GENERATIONS).
7.This is just a simple example of a Genetic Algorithm for the One Max Problem. For more complex problems, you may need to adjust the parameters and genetic operators according to the specific context of your problem.