Genetic algorithms
Another class of black-box methods that has recently become a popular alternative to the value-based and policy gradient methods is genetic algorithms (GA). It is a large family of optimization methods with more than two decades of history behind it and a simple core idea of generating a population of N individuals, each of which is evaluated with the fitness function. Every individual means some combination of model parameters. Then, some subset of top performers is used to produce (called mutation) the next generation of the population. This process is repeated until we're satisfied with the performance of our population.
There are a lot of different methods in the GA family, for example, how to complete the mutation of the individuals for the next generation or how to rank the performers. Here, we will consider the simple GA method with some extensions, published in the paper by Felipe Petroski Such, Vashisht Madhavan, and others called Deep Neuroevolution...
