  Real-valued genotypes together with the variation operators, mutation and
crossover, constitute some of the fundamental building blocks of Evolutionary
Algorithms. Real-valued genotypes are utilized in a broad range of contexts,
from weights in Artificial Neural Networks to parameters in robot control
systems. Shared between most uses of real-valued genomes is the need for
limiting the range of individual parameters to allowable bounds. In this paper
we will illustrate the challenge of limiting the parameters of real-valued
genomes and analyse the most promising method to properly limit these values.
We utilize both empirical as well as benchmark examples to demonstrate the
utility of the proposed method and through a literature review show how the
insight of this paper could impact other research within the field. The
proposed method requires minimal intervention from Evolutionary Algorithm
practitioners and behaves well under repeated application of variation
operators, leading to better theoretical properties as well as significant
differences in well-known benchmarks.
