readme

this file is a zip file for multi objective optimization by matlab.

This Matlab toolbox uses genetic algorithms to solve optimization
problems. It is based on the "Genetic Algorithm Optimization Toolbox" by
C.R. Houck, J.A. Joines and M.G. Kay. The expansion of the original
toolbox facilitates using the algorithm for solving problems involving
different types of design variables. The four types of variables that
can be used are

- binary: The value is either 0 or 1 - the traditional genetic algorithm
  representation.
- independent integer: The value can be any integer between the lower and
  upper defined boundaries (also negatives), yet the values have no
  connection. Think of this representation as a set of options, e.g., the
  colors red, green and blue.
- dependent integer: Again, the value can be any integer between the lower
  and upper defined boundaries, however the values represent a real-life
  number, e.g., the number of wheels on an automobile.
- floating point: The value represents a real-life number directly, e.g.,
  the optimal dimension of something.
  
Have a look at the example runMultiVerification to get started. Also,
you can find information in the comments of the initializemultiga and
multiga files and on my homepage at <http://kerry.lothrop.de/>.