** Next:** 1 Regression Functions and
** Up:** 4 Application Domains
** Previous:** 3 Parity Related Studies
** Contents**

In many scientific disciplines, finding a model explaining the
relationship of large and complex sets of data is required. However,
the task is often difficult and many methods have been employed
to uncover models of data. The
domain of symbolic regression is one method where
input and output pairs are used to infer a functional model,
typically consisting of a function and its coefficients.
Keijzer (2001) researched the application of
genetic programming on the
Regression domain in the context of ``scientific discovery'',
demonstrating several ways to improve its application.
The Regression problem attempts to find a program that
approximates a target function.
A target function is applied to domain values, ,
in a pre-determined
range. The resulting values are then compared with the candidate
program's value
upon the same values.
This thesis uses the Quartic polynomial,

the Rastrigin function,

the Binomial-3 polynomial,

and several random
polynomials (described in Chapter 5).
For the Rastrigin instance, is in the range
, for the
Quartic, Binomial-3 and random polynomial instances, is in the range
.
The number of () pairs is
for both the Quartic and Rastrigin instances and for
the Binomial-3 and random polynomial instances.
Figure 2.3 shows the Quartic polynomial and the
Rastrigin function ().

**Subsections**

** Next:** 1 Regression Functions and
** Up:** 4 Application Domains
** Previous:** 3 Parity Related Studies
** Contents**
S Gustafson
2004-05-20