Code Growth and Neutral Variations

Abstract

This chapter brings together theories about neutral variations and code growth in genetic programming. We argue that neutral variations are important for the growth of code in GP runs. Other existing theories about code growth are verified for linear GP and are partly reevaluated from a different perspective.

In evolutionary computation neutral variations are argued to explore flat regions of the fitness landscape while non-neutral variations exploit regions with gradient information. We investigate the influence of different variation effects on growth of code and the prediction quality for different kinds of variation operators. It is well known that a high proportion of neutral code (introns) in genetic programs may increase the probability for variations to be neutral. But which type of variation creates the introns in the first place? For linear GP with minimum mutation, step size results show that neutral variations almost exclusively represent a cause for (rather than a result of) the emergence and growth of intron code. This part of the chapter is a continuation of our earlier studies [25].

We also examine different linear genetic operators regarding an implicit length bias. In contrast to an explicit bias, implicit bias does not result from the dynamics of the operator alone, but requires the existence of a fitness pressure.

We will close this chapter with a discussion about how to control code growth in linear GP. Different approaches are reviewed including variation-based methods and selection-based methods. Both may be applied specifically to effective code and/or to noneffective code.