Genetic Algorithms Demystified - How Algorithms Evolve

Key Takeaways

a genetic algorithm is a search heuristic algorithm that is inspired by Charles Darwin's theory of natural evolution it reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation introduced in 1960 it is commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation crossover and selection the process begins with a set of individuals which is called a population each individual is a solution to the problem you want to solve an individual is characterized by a set of parameters variables known as genes usually represented with binary values zeros and ones genes are joined into a string to form a chromosome solution we say that we encode the genes in a chromosome then the Fitness function determines how fit an individual is the ability of an individual to compete with other individuals it gives a fitness score to each individual the probability that an individual will be selected for reproduction is based on its Fitness score which is done in the selection phase by pairs of two called the parents that are selected for reproduction then there is the crossover it is the most significant phase in a genetic algorithm for each pair of parents to be mated a crossover point is chosen at random from within the genes for example consider the crossover point to be three years shown in the picture above offspring are created by exchanging the genes of parents among themselves until the crossover point is reached the new offspring are added to the population at the end of the loop there's the mutation only some of their genes can be subjected to aid with a low random probability this implies that some of the bits in the bit string can be flipped mutation occurs to maintain diversity within the population and prevent premature convergence this process keeps on iterating and at the end a generation with the fittest individuals will be found finally the algorithm terminates if the population has converged then it is said that the genetic algorithm has provided a set of solutions to our problem but what is the purpose of a genetic algorithm in artificial intelligence selection of the optimal parameters and datas to use for machine learning tasks is challenging some results may be bad not because the data is noisy or the used learning algorithm is weak but due to the bad selection of the parameters values genetic algorithms are one of the simplest random based evolutionary algorithms that are used for optimizing your data set and hyper parameters please leave a like if you learn something and subscribe to the channel to not miss any terms clearly explained [Music]