genetic algorithm

special thaks to Daniel Shiffman and his work Nature of code.

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.

process involved in genetic algorithm:-

SETUP:
1. Initialize. Create a population of N elements, each with randomly generated DNA.
LOOP:
2. Selection. Evaluate the fitness of each element of the population and build a matingpool.
3. Reproduction. Repeat N times:
   1. Pick two parents with probability according to relative fitness.
   2. Crossover—create a “child” by combining the DNA of these two parents.
   3. Mutation—mutate the child’s DNA based on a given probability.
   4. Add the new child to a new population.
4. Replace the old population with the new population and return to Step 2.