On Fitness Distributions and Expected Fitness Gain of Mutation Rates in Parallel Evolutionary Algorithms

  • David W. Corne
  • Martin J. Oates
  • Douglas B. Kell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)


Setting the mutation rate for an evolutionary algorithm (EA) is confounded by many issues. Here we investigate mutation rates mainly in the context of large-population-parallelism. We justify the notion that high rates achieve better results, using underlying theory which notices that parallelization favourably alters the fitness distribution of a mutation operator. We derive an expression which sets out how this is changed in terms of the level of parallelization, and derive further expressions that allow us to adapt the mutation rate in a principled way by exploiting online-sampled landscape information. The adaptation technique (called RAGE– Rate Adaptation with Gain Expectation) shows promising preliminary results. Our motivation is the field of Directed Evolution (DE), which uses large-scale parallel EAs for limited numbers of generations to evolve novel proteins. RAGE is highly suitable for DE, and is applicable to large-scale parallel EAs in general.


Mutation Rate Direct Evolution Fitness Distribution Adaptation Technique Promising Preliminary Result 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • David W. Corne
    • 1
  • Martin J. Oates
    • 2
  • Douglas B. Kell
    • 3
  1. 1.Department of Computer ScienceUniversity of ReadingUK
  2. 2.Evosolve LtdStowmarket, SuffolkUK
  3. 3.Institute of Biological SciencesUniversity of WalesAberystwythUK

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