When Should A Genetic Algorithm Be Used

The GA literature describes a large number of successful applications, but there are also many cases in which GAs perform poorly. Given a particular potential application, how do we know if a GA is good method to use? There is no rigorous answer, though many researchers share the intuitions that if the space to be searched is large, is known not to be perfectly smooth and unimodal (i.e., consists of a single smooth "hill"), or is not well understood, or if the fitness function is noisy, and if the task does not require a global optimum to be found—i.e., if quickly finding a sufficiently good solution is enough—a GA will have a good chance of being competitive with or surpassing other "weak" methods (methods that do not use domain-specific knowledge in their search procedure). If a space is not large, then it can be searched exhaustively, and one can be sure that the best possible solution has been found, whereas a GA might converge on a local optimum rather than on the globally best solution. If the space is smooth or unimodal, a gradient-ascent algorithm such as steepest-ascent hill climbing will be much more efficient than a GA in exploiting the space's smoothness. If the space is well understood (as is the space for the well-known Traveling Salesman problem, for example), search methods using domain-specific heuristics can often be designed to outperform any general-purpose method such as a GA. If the fitness function is noisy (e.g., if it involves taking error-prone measurements from a real-world process such as the vision system of a robot), a one-candidate-solution-at-a-time search method such as simple hill climbing might be irrecoverably led astray by the noise, but GAs, since they work by accumulating fitness statistics over many generations, are thought to perform robustly in the presence of small amounts of noise.

These intuitions, of course, do not rigorously predict when a GA will be an effective search procedure competitive with other procedures. A GA's performance will depend very much on details such as the method for encoding candidate solutions, the operators, the parameter settings, and the particular criterion for success. The theoretical work described in the previous chapter has not yet provided very useful predictions. In this chapter I survey a number of different practical approaches to using GAs without giving theoretical justifications.

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