title: Fitness landscapes and evolvability.
abstract: In this paper, we develop techniques based on evolvability statistics of the fitness landscape surrounding sampled solutions. Averaging the measures over a sample of equal fitness solutions allows us to build up fitness evolvability portraits of the fitness landscape, which we show can be used to compare both the ruggedness and neutrality in a set of tunably rugged and tunably neutral landscapes. We further show that the techniques can be used with solution samples collected through both random sampling of the landscapes and online sampling during optimization. Finally, we apply the techniques to two real evolutionary electronics search spaces and highlight differences between the two search spaces, comparing with the time taken to find good solutions through search.
authors.affiliation: Centre for Computational Neuroscience and Robotics, School of Biological Sciences, University of Sussex, Brighton, UK. toms@cogs.susx.ac.uk
authors.forename: Tom
authors.initials: T
authors.lastname: Smith
authors.forename: Phil
authors.initials: P
authors.lastname: Husbands
authors.forename: Paul
authors.initials: P
authors.lastname: Layzell
authors.forename: Michael
authors.initials: M
authors.lastname: O'Shea
mesh_terms: D005075:Biological Evolution; D017753:Ecosystem; D019143:Evolution, Molecular; D005843:Geography; D008962:Models, Theoretical; D009154:Mutation; D009690:Nucleic Acid Conformation; D011336:Probability; D012313:RNA / Q000737:chemistry
chemical_list: D012313:RNA