Removal Bias: a New Cause of Code Growth in Tree Based Evolutionary Programming (1998) [21 citations — 4 self]
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author = {Terence Soule and James A. Foster},
title = {Removal Bias: a New Cause of Code Growth in Tree Based Evolutionary Programming},
booktitle = {In 1998 IEEE International Conference on Evolutionary Computation},
year = {1998},
pages = {781--186},
publisher = {IEEE Press}
}
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Abstract:
This paper 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. 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. Keywords--- Code growth, variable length representations, removal bias, parsimony I. Introduction The rapid growth of fitness neutral code in genetic programming (GP), often referred to as code growth or code bloat, is a well documented phenomenon [1], [2], [3], [4], [5]. Code growth is a serious issue because larger programs require additional memory and CPU time, often taxing available resources and limiting GP usefulness. Add...
