Here we provide an Eclipse plugin for programmers that allows to evolve (i.e., to get in an automated way) simple functions that meet specified requirements (pass through given points).
Downloads
Installation
To install this Eclipse plug-in:
- Install Eclipse if it is not installed already (The plug-in has been
tested with Eclipse 3.6, other versions are not officially supported).
- Copy file FunctionGenerator_x.x.x.jar to the plugins folder in Eclipse
installation directory (The x.x.x part of the name denotes plug-in version
number).
- Run Eclipse.
Usage
To run the plug-in, you can either click the FG icon on the toolbar, or select
Function Generator -> Generate Function in the main menu.
The main program window should be shown. There are many options there, from
the top:
- Problem type – defines that floating-point or integer arithmetic should be used.
- Constraints – constraint point for symbolic regression. Usually more
points give more accurate results, but increase computation times. The
constraints can be added manually, or by using the linear scaling problem wizard.
To use the wizard, click on the "Linear scaling problem" button.
- Basic settings
- Size of population – number of individuals in population. Usually
more individuals cover search space better, but increase computation
times and memory consumption. Default: 25000.
- Number of generations – number of generations for evolutionary
algorithm. Default: 50.
- Max depth of tree – maximum depth of the genetic programming tree that represents the function expression you look for. Too big tree size may cause overfitting (lack of generalization). The limit set too low may disallow expressions complex enough to describe your data. Default: 6.
- Available functions – list of available base functions. Function sets for
floating-point and integer problems differ a bit.
When problem definition is done, click the OK button to begin computation.
Linear scaling problem wizard lets you define constraints on
independent variable x0 and dependent variable y. The constraints can be
constant or variable (if a variable constraint is chosen, then a new variable – a new dimension will
be added to the problem). The user input interpretation is shown at the bottom.
When computation begins, the progress window is shown. Each output entry
contains fitness value of the best individual in generation (0 – best fitness, ∞
- worst fitness), its adjusted version using the following equation: adjusted =
1/(1+fitness), and hits points – the number of fulfilled constraints.
When evolution is done, the result window is shown. A sample result function is underlined in the screenshot below.
