A Comprehensive View of Fitness Landscapes with Neutrality and Fitness Clouds

  • Leonardo Vanneschi
  • Marco Tomassini
  • Philippe Collard
  • Sébastien Vérel
  • Yuri Pirola
  • Giancarlo Mauri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4445)


We define a set of measures that capture some different aspects of neutrality in evolutionary algorithms fitness landscapes from a qualitative point of view. If considered all together, these measures offer a rather complete picture of the characteristics of fitness landscapes bound to neutrality and may be used as broad indicators of problem hardness. We compare the results returned by these measures with the ones of negative slope coefficient, a quantitative measure of problem hardness that has been recently defined and with success rate statistics on a well known genetic programming benchmark: the multiplexer problem. In order to efficaciously study the search space, we use a sampling technique that has recently been introduced and we show its suitability on this problem.


Genetic Program Boolean Function Fitness Landscape Neutral Network Solution Ratio 
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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© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Leonardo Vanneschi
    • 1
  • Marco Tomassini
    • 2
  • Philippe Collard
    • 3
  • Sébastien Vérel
    • 3
  • Yuri Pirola
    • 1
  • Giancarlo Mauri
    • 1
  1. 1.Dipartimento di Informatica, Sistemistica e Comunicazione (D.I.S.Co.), University of Milan-Bicocca, MilanItaly
  2. 2.Computer Systems Department, University of Lausanne, LausanneSwitzerland
  3. 3.I3S Laboratory, University of Nice, Sophia AntipolisFrance

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