Franken-swarm: grammatical evolution for the automatic generation of swarm-like meta-heuristics.

In the last 20 years, literally dozens of optimization algorithms based on swarm intelligence have been proposed. Particle Swarm Optimization, Artificial Bee Colony, Cuckoo Search, Firefly Optimization, and Cat Swarm Optimization are just a small sample of the exuberance of swarm-like algorithms. Although they differ in implementation details, they all share a common structure: an update rule is applied to each solution, followed by a drop rule that decides whether to keep the updated solution or not. In this poster we explore the idea of automatically generating swarm-like optimizers. Our proposal is divided in two stages: First we decompose popular, human-crafted, swarm-like optimizers such as PSO, CS, ABC (as well as DE/GA) into a list of basic rules. Second, we use Grammatical Evolution to procedurally generate variations on this base structure by recombining these operators. We generate three instances of algorithms, and observe that they have comparable performance to DE and PSO. Our framework will be useful to gain insight on the design space of meta-heuristics and the nature of swarm-like algorithms.