Genetic Algorithms (GAs) 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.

While 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.

Yet we do not accept the genetic algorithm method by appeal to this beauty-of-nature argument alone. GAs are theoretically and empirically proven to provide robust search in complex spaces.

In order for GAs to surpass their more traditional cousins in the quest for robustness, GAs differ in some very fundamental ways:

  1. GAs work with a coding of the parameter set and not the parameters themselves. They exploit coding similarities in a very general way. As a result, they are largely unconstrained by the limitations of other methods (e.g. continuity, derivative existence, unimodality etc.)
  2. GAs search from a population of points and not just a single point. In many optimization methods, we move gingerly from a single point in the decision space to the next using some transition rule to determine the next point. This point-to-point method is dangerous because it is a perfect prescription for locating false peaks in multimodal (many-peaked) search spaces. By contrast, GAs work from a rich database of points simultaneously (a population of strings), climbing many peaks in parallel; thus the probability of finding a false peak is reduced.
  3. GAs use payoff (fitness function) information, not derivatives or other auxiliary knowledge. GAs are blind. To perform an effective search for better and better structures, they only require payoff values (fitness function values) associated with individual strings. This characteristic makes a GA a more canonical method than most search schemes.
  4. GAs use probabilistic transition rules, not deterministic rules. GAs use random choice as a tool to guide their search toward regions of the search space with likely improvement.

Taken together, these four differences, contribute to a genetic algorithms robustness and resulting advantage over other more commonly used search techniques.

For more information on the mechanics of Genetic Algorithms, we recommend the following book:

D.E.Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Welsley.  

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