ABSTRACT
In a natural swarm intelligence system, direct interaction and communication occurs through food exchange, visual contact, and chemical contact (pheromones). Such interactions can be easily imitated in artificial swarm intelligence. In particle swarm optimization, individuals are the “particles” and the population is the “swarm.” Genetic programming is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. The idea of genetic programming is to evolve a population of computer programs. Genetic programming does not have to be for mathematical function fitting. Evolutionary algorithms include genetic algorithms and genetic programming. Genetic programming and genetic algorithms are very similar in that they both use evolution by comparing the fitness of each candidate in a population of potential candidates over many generations. Swarm intelligence and evolutionary artificial intelligence have found applications in drug discovery and development and in artificial general intelligence.
