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The drawback of the GA approach is that the solution found will be a solution approaching the absolute optimum solution, but not necessarily the absolute optimum solution itself. This drawback, however, is handsomely offset by the processing power and time savings in cases with a large number of possible solutions.
In general, GA's work is primarily about two abstracts: an Individual (or Genome) and an Algorithm (i.e. Genetic Algorithm itself). Each Genome instance represents a single unique inputs combination, while GA itself defines how the evolution should take place. The GA uses a given trading strategy to determine how 'fit' a genome is for survival, e.g. how much Net Profit does an inputs combination generates in case Net Profit was selected as an Optimization Criteria.
<br>Here are some GA definitions that help in understanding the process:
