A evolutionary algorithm library for Dart. Given a population of agents, an evaluator (fitness function), and time, the algorithm will evolve the population until it crosses given fitness threshold.
Evolution Strategies, sometimes also referred to as Evolutionary Strategies, and Evolutionary Programming are search paradigms inspired by the principles of biological evolution. They belong to the family of evolutionary algorithms that address optimization problems by implementing a repeated process of stochastic variations followed by selection: in each iteration (or generation), new candidate solutions (or offspring) are generated from previous candidate solutions (parents), their fitness is evaluated, and the better candidate solutions are selected to become the generators for the next iteration.
Read more about differential evolution on Wikipedia.