Bio-inspired algorithms

Bio-inspired computing is a field of study that brings together the areas of computer science, biology, and mathematics; it pursuits two closely related goals: the use of computers to model living phenomena and the use of these phenomena as a source of inspiration for optimizing the use of computing resources. The growing number of bio-inspired optimization algorithms gaining notoriety each year is proof of this approach’s efficiency. One of the characteristics common to many bio-inspired algorithms is the capability to deal with high-dimensional non-linear problems, such as those commonly found in biosignal analysis. For this reason, knowing and understanding the basic mechanism behind these algorithms is a must for experts working with biosignals. This chapter presents an overview of six bio-inspired algorithms, namely genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search (CS), artificial bee colony (ABC), and flower pollination algorithm (FPA). All six algorithms are well known for their effectiveness in dealing with high-dimensional cost functions, and together offer a fair panorama on bio-inspired optimization’s state of the art.