genetic algorithm is an optimization technique introduced
by John Holland in 1975. The principle of this technique is to simulate
the evolution in nature.
a good solution in complex problems can be very difficult because
your computer is not fast enough to try all the possibilities. The
genetic algorithm generates a pool of solutions, some will be better
than the others and the cross of these solutions could yield a better
we take the case of the animal world, we can consider one chromosome
of a particular beast as one possible solution for the species.
We will see how we can create a more powerful animal from two who
had some good characteristics.
From the group of beasts we had, we will
select one with a good sight and one with strong feet. As seen in
the following figure (Classic crossover) these two parents could
generate two offspring, one would be better than either of the two
creators, and one will probably have no particular charateristics.
have now generated a better animal. The main idea of the genetic
algorithm is to cross this new animal again with others, always
attempting to create better creatures.
The above is the principle of the classic genetic algorithm. Different
real-world optimization problems require different enhancements
to the basic technique. Optimal Design's expertise with the technique
allows us to design the proper enhancement for each target problem.
For instance, our proprietary grouping genetic algorithm is well
suited for grouping problems.
Details of our methods can be found
in the book of our co-founder