When Holland (1975) proposed genetic algorithms, he envisioned them as methods that were going to be efficient, easy to use, and applicable to a wide range of problems. But one thing that stands out from the current literature, is that genetic algorithms seem to require quite a bit of expertise in order to make them work well for a particular application. The expertise is needed because users are generally clueless on how to decide among the various codings and operators, as well as on deciding on a good set of parameter values for the GA. In the end, instead of being a robust and an easy-to-use method, the genetic algorithm turns out to be a method that needs a lot of tuning and parameter fiddling. This state of affairs is a kind of a paradox, and contradicts Holland's original goals.

The decisions that a user must make before applying a GA can be grouped in two categories. The first, is the choice of an appropriate coding and operators. The second, is the choice of appropriate parameter settings.

From the user's point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to hand-in the problem to a blackbox algorithm, and simply press a start button. We investigate the development of a GA that fulfils this requirement. It has no parameters whatsoever. The development of the algorithm takes into account several aspects of the theory of GAs, including previous research works on population sizing, the schema theorem, building block mixing, and genetic drift.

If Genetic Algorithms should be made accessible for a broader public, further convergence criteria and proofs must be developed - in particular in order to place the selection of numerous parameters on a solid base.
 

Related Literature (Some of the publications which I have read.)

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