Contents What are Genetic Algorithms? From Biology … Evolution … To Genetic Algorithms Demo
What are Genetic Algorithms? A method of solving Optimization Problems –Exponentially large set of solutions –Easy to compute cost or value Search algorithm (looking for the optimum) Very similar to random search?! Population- based –We start with a set of possible solutions (initial population) and evolve it to get to the optimum –Also called Evolutionary Algorithms Based on evolution in biology
From Biology … Charles Darwin (1859) Natural selection, “survival of the fittest” Improvement of species Can we use the same idea to get an optimal solution?
Evolution To implement optimization as evolution, We need Mapping features to genes, showing each individual with a chromosome An initial population Have a function to measure fitness same as what we want to optimize Implement and apply Reproduction Replace offspring in old generation Have an exit condition for looping over generations
Initial Population Representation of possible solutions as chromosomes –Binary –Real –etc. Random initial population If not random stuck in local optima
Recombination (crossover) Random crossover points Inheriting genes from one parent
Mutation Random Mutation Point Changing gene value to a random value
… to Genetic Algorithms BEGIN /* genetic algorithm*/ Generate initial population ; Compute fitness of each individual ; LOOP Select individuals from old generations for mating ; Create offspring by applying recombination and/or mutation to the selected individuals ; Compute fitness of the new individuals ; Kill old individuals,insert offspring in new generation ; IF Population has converged THEN exit loop; END LOOP END