Neutral Fitness Landscape in the Cellular Automata Majority Problem

  • S. Verel
  • P. Collard
  • M. Tomassini
  • L. Vanneschi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4173)


We study in detail the fitness landscape of a difficult cellular automata computational task: the majority problem. Our results show why this problem landscape is so hard to search, and we quantify the large degree of neutrality found in various ways. We show that a particular subspace of the solution space, called the ”Olympus”, is where good solutions concentrate, and give measures to quantitatively characterize this subspace.


Genetic Programming Cellular Automaton Fitness Landscape Neutral Network Coevolutionary Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. Verel
    • 1
  • P. Collard
    • 1
  • M. Tomassini
    • 2
  • L. Vanneschi
    • 3
  1. 1.Université de Nice-Sophia Antipolis/CNRS 
  2. 2.University of Lausanne 
  3. 3.University of Milano 

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