ai with python genetic algorithms this discusses genetic algorithms of ai in detail what are genetic algorithmsgenetic algorithms (gasare search based algorithms based on the concepts of natural selection and genetics gas are subset of much larger branch of computation known as evolutionary computation gas were developed by john holland and his students and colleagues at the university of michiganmost notably david goldberg it has since been tried on various optimization problems with high degree of success in gaswe have pool of possible solutions to the given problem these solutions then undergo recombination and mutation (like in natural genetics)produces new childrenand the process is repeated for various generations each individual (or candidate solutionis assigned fitness value (based on its objective function valueand the fitter individuals are given higher chance to mate and yield fitter individuals this is in line with the darwinian theory of survival of the fittest thusit keeps evolving better individuals or solutions over generationstill it reaches stopping criterion genetic algorithms are sufficiently randomized in naturebut they perform much better than random local search (where we just try random solutionskeeping track of the best so far)as they exploit historical information as well how to use ga for optimization problemsoptimization is an action of making designsituationresource and systemas effective as possible the following block diagram shows the optimization processinput function output stages of ga mechanism for optimization process the following is sequence of steps of ga mechanism when used for optimization of problems step generate the initial population randomly step select the initial solution with best fitness values step recombine the selected solutions using mutation and crossover operators step insert an offspring into the population