Smooth operator? Understanding and visualising mutation bias

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

11 Citations (Scopus)


The potential for mutation operators to adversely affect the behaviour of evolutionary algorithms is demonstrated for both real-valued and discrete-valued genotypes. Attention is drawn to the utility of effective visualisation techniques and explanatory concepts in identifying and understanding these biases. The skewness of a mutation distribution is identified as a crucial determinant of its bias. For redundant discrete genotype-phenotype mappings intended to exploit neutrality in genotype space, it is demonstrated that in addition to the mere extent of phenotypic connectivity achieved by these schemes, the distribution of phenotypic connectivity may be critical in determining whether neutral networks improve the ability of an evolutionary algorithm overall.
Original languageEnglish
Title of host publicationAdvances in Artificial Life: Proceedings of the Sixth European Conference on Artificial Life (ECAL 2001)
EditorsJ. Kelemen, P. Sosik
PublisherSpringer Berlin Heidelberg
Number of pages11
Publication statusPublished - 2001


Dive into the research topics of 'Smooth operator? Understanding and visualising mutation bias'. Together they form a unique fingerprint.

Cite this