About the Genetic Algorithm
To make a genetic algorithm, you
create a set of n models that independently try to "optimize" their way to the
best solution. While the memory and time requirements are rather large, genetic
algorithms are a good way to converge on the best maxima in the case of
uncertain system properties and poorly defined unit operations for
calculations.
False maxima can severely
complicate some modeling techniques. But the more models there are in the
program, the less likely that all of them will fail. While GA's don't
necessarily make a maximum provable, they usually do the trick when run with
suitable attributes.
Problem Description
The genetic algorithm program was
constructed in LISP, a linkedlistbased language. The problem in this case is
to find the bestfit equation for a set of data. But...the bestfit equation
has both polynomial terms ranging from x^(3) to x^(4), as well as exponential
terms ranging from exp(3x) to exp(4x). Faced with such nonlinear terms, least
squares analysis becomes rather tough using other optimization methods.
The genetic algorithm busts this
problem by exchanging information among the best models, promoting the models
with less error as more likely to "reproduce" in the next round. A mutation
factor is also present.
Download
Genetic Algorithm Source Code
Projects
Downloads
