Genetic algorithms <\/strong>used to be a very popular branch of algorithms during the 90s early 00s. Genetic algorithms<\/a> used to be one of the cornerstones of what is called computational intelligence<\/strong>. Computational intelligence<\/a> is a subfield of artificial intelligence<\/strong> based mainly on the idea of creating intelligence by mimicking natural processes. For example, swarms display a certain kind of intelligence as they self-organise in response to hazards. Similarly, evolution is an optimisation algorithm running in nature, that has come up with ingenious ways to preserve life. Our brain is also a biological machine that displays intelligence, and we could mimic its neural processes in order to try and recreate intelligence in silico.<\/p>\n\n\n\n <\/p>\n\n\n\n For those of you who don\u2019t know what a genetic algorithm is, it is basically an optimisation method that mimics evolution<\/strong>. You have a set of candidate solutions (called a population). The best solutions get combined with each other (mating) and also some of those get randomly mutated. This lets the algorithm both use good solutions, but also explore new options. Genetic algorithms became famous through the work of Holland<\/a>, who, in 1975, wrote up the book Adaptation in Natural and Artificial Systems<\/a>. Other evolutionary inspired algorithms showed up through the years, such as evolution strategies<\/a> and particle swarm optimisation<\/a>. The video below by MATLAB explains how genetic algorithms work, for any of you who are curious.<\/p>\n\n\n\n