Algorithms (GA’s) are search algorithms based on the mechanics of
natural selection. They combine survival of the fittest principles with a structured yet randomized information exchange
to form a
search algorithm with some innovative flair of human search.
traditional optimization search methods seek local optima, they are
insufficiently robust when applied to noisy financial data. Where robust
performance is desired, nature does it better; the secrets of adaption and
survival are best learned from the careful study of biological example.
we do not accept the genetic algorithm method by appeal to this
beauty-of-nature argument alone. GA’s are theoretically and empirically
proven to provide robust search in complex spaces.
order for GA’s to surpass their more traditional cousins in the quest
for robustness, GA’s differ in some very fundamental ways:
together, these four differences, contribute to a genetic algorithm’s
robustness and resulting advantage over other more commonly used search
more information on the mechanics of Genetic Algorithms, we recommend the
Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Welsley.