MACHINE LEARNING TECHNIQUES - LASA

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• Biological Sciences: Dynamics of groups
In Robotics, GA have been used successfully to optimize parameters of controllers in order to
evolve the Artificial Neural Networks Controllers. The GA is used to determine the weights or the
topology of the network.
8.1 Principle
Genetic Algorithms provide a form of simulated evolution. The basic idea goes along the following
steps:
• Start from a basic random population of parents
• Apply a process of selection
• Breed pairs of parents
• Produce pairs of children with only the good properties of the parents
• Reject bad children
The process leads to the evolution of a population of children that are better than the parents.
8.2 Encoding
To use a genetic algorithm, you must encode solutions to your problem in a structure that can be
stored in the computer. This object is a genome (or chromosome). Thus, the first step before
using a GA consists in a) defining a representation of the problem (this is often the most difficult
step, as it requires some a priori knowledge on where the important features of the problem lie);
b) defining the parameters to optimize (e.g. the weights in a neural networks); c) encoding the
parameters into a chromosome; and d) creating a whole population, i.e. generate several random
chromosomes.
You can use any representation for the individual genomes in the genetic algorithm. Holland
worked primarily with strings of bits, but you can use arrays, trees, lists, or any other object.
© A.G.Billard 2004 – Last Update March 2011