Evolutionary Algorithms

author = "Bob McKay",

title = "Evolutionary Algorithms",

editor = "Sven Erik Jorgensen and Brian Fath",

booktitle = "Encyclopedia of Ecology",

publisher = "Academic Press",

address = "Oxford",

year = "2008",

pages = "1464--1472",

isbn13 = "978-0-08-045405-4",

DOI = "

doi:10.1016/B978-008045405-4.00157-9",

URL = "

http://www.sciencedirect.com/science/article/B9636-4SY6CH0-DJ/2/11adb71275768a6e15d7c1e63d29d946",

keywords = "genetic algorithms, genetic programming, Ant colony optimization, Artificial immune system, Ecological modeling, Estimation of distribution algorithm, Evolution strategy, Evolutionary algorithm, Evolutionary computation, Machine learning, Messy genetic algorithm, Model fitting, Optimization, Particle swarm algorithm",

abstract = "Evolutionary algorithms are widely used in ecological modeling. Darwin's theory of natural selection can be viewed as an algorithm for evolving fit organisms, with fit in this context meaning survivable. The insight of evolutionary computation is that the same algorithm can be applied to populations of problem solutions, with the fitness metric being defined so as to suit the requirements of the user. Subsequent advances in evolutionary theory can then be viewed as algorithm refinements, worthy of evaluation for possible inclusion in the method. Applications in ecological modeling range from tuning parameters of predefined models to fit the data, through generating predictive and/or explanatory models directly from the data, to modeling the coevolutionary processes in ecological systems. The original systems, evolution strategies and genetic algorithms (GAs), have since been joined by a family of related algorithms, notably classifier systems, genetic programming, messy GAs, estimation of distribution algorithms, ant colony and particle swarm algorithms, and artificial immune systems, all of which have something to offer the ecological modeler.",

notes = "Author given as {"}B{"} McKay",