It has been recognised from the early stages in GP that the choice of genetic recombination operator can influence the growth in code size. Sims (1993) was an early attempt to limit code growth using a set of mutation operators that were designed to bias against an increase in code size. Rosca and Ballard (1996) approached the problem from a different angle, suggesting that the genetic operators themselves change dynamically to deal with increases in the size of members of the population selected for recombination. Their approach focuses on the effect of varying the probability of destructive genetic operators relative to the complexity of structures present in the population.
Standard crossover should not be regarded as the best method of genetic recombination simply because it is the most widely used. Alternate forms are possible, but existing off-the-shelf GP systems may only provide standard crossover (or perhaps also mutation), and implementing nonstandard forms of crossover can be time consuming and may produce no extra benefits for a data mining project.
However, research is taking crossover in the direction of domain-specific forms of crossover (Langdon, 2000) and it is likely that in the future crossover may be more directed to the domain of the problem.
SUMMARY
This chapter has outlined areas in which GP may be successfully applied to data mining including clustering and categorisation. GP has faced problems with scaling and these have been investigated. Some of the recent research into identifying why the scaling problem exists and what can be done to improve the performance of GP on harder problems has also been described.
GP can be a potentially useful tool for data mining applications, but its strengths and weaknesses need to be clearly understood. This chapter describes some of the common problems that are encountered in using GP and suggests potential solutions.
Code growth is identified as a major problem GP and four methods of attack are described in increasing order of difficulty. In trying to apply GP to any problem, it is important to identify the method that is most likely to lead to an enhanced search capability and apply it. However, it is important also to recognise that some methods of enhancing search are easier to implement than others and it is useful to try easy
methods first. In the future many of these methods are almost certain to be provided without any explicit effort by the user.
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