evolutionary algorithm optimization

September 4, 2008

(This article was first published on Quantitative Finance Collector, and kindly contributed to R-bloggers)

In the post Optimization packages dozens of optimization routines can be downloaded, here I am going to share a special optimization method: evolutionary algorithm.

Evolutionary algorithms (EAs) are search methods that take their inspiration from natural selection and survival of the fittest in the biological world. EAs differ from more traditional optimization techniques in that they involve a search from a "population" of solutions, not from a single point. Each iteration of an EA involves a competitive selection that weeds out poor solutions. The solutions with high "fitness" are "recombined" with other solutions by swaping parts of a solution with another. Solutions are also "mutated" by making a small change to a single element of the solution. Recombination and mutation are used to generate new solutions that are biased towards regions of the space for which good solutions have already been seen.

This R package provides the DEoptim function which performs Differential Evolution Optimization (evolutionary algorithm), for detail check http://cran.r-project.org/web/packages/DEoptim/index.html.
wiki(Evolutionary algorithm)
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