Removal bias: a new cause of code growth in tree based evolutionary programming

Abstract

Presents a new cause of code growth, termed removal bias. We show that growth due to removal bias can be expected to occur whenever operations which remove and replace a variable-sized section of code, e.g. crossover or subtree mutation, are used in an evolutionary paradigm. Two forms of non-destructive crossover are used to examine the causes of code growth. The results support the protective value of inviable code and removal bias as two distinct causes of code growth. Both causes of code growth are shown to exist in at least two different problems.