Exploratory Toolkit for Evolutionary and Swarm-Based Optimization
Namrata Khemka, Christian Jacob · The Mathematica Journal · 2010
Optimization of parameters or ’systems ’ in general plays an ever−increasing role in mathematics, economics, engineering, and life sciences. As a result, a wide variety of both traditional analytical, mathematical and non−traditional algorithmic approaches have been introduced to solve challenging and practically relevant optimization problems. Evolutionary optimization methods�namely, genetic algorithms, genetic programming, and evolution strategies�represent a category of non−traditional optimization algorithms drawing inspirations from the process of natural evolution. Particle swarm optimization represents another set of more recently developed algorithmic optimizers inspired by social behaviours of organisms such as birds [8] and social insects. These new evolutionary approaches in optimization are now entering the stage, and are thus far very successful in solving real−world optimization problems [12]. Although these evolutionary approaches share many concepts, each one has its strengths and weaknesses. The best way to understand these techniques is through