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A Reverse Genetic Approach to Test Functional Redundancy During Embryogenesis
06:59

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Published on: August 11, 2010

A study of redundancy and neutrality in evolutionary optimization.

Marisol B Correia1

  • 1ESGHT, University of Algarve, Faro, Portugal; and CEG-IST, Technical University of Lisbon, Lisbon, Portugal. mcorreia@ualg.pt

Evolutionary Computation
|September 25, 2012
PubMed
Summary
This summary is machine-generated.

Redundant representations significantly impact evolutionary search, with the induced phenotypic neighborhood being more influential than neutrality. Optimal representations on NK landscapes avoid extreme quality indicator values.

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Area of Science:

  • Computational intelligence
  • Evolutionary computation
  • Optimization algorithms

Background:

  • The role of redundant representations in evolutionary search remains debated.
  • Some research suggests benefits, while others find redundancy ineffective for optimization.

Purpose of the Study:

  • To investigate the influence of redundancy and neutrality in binary representations on evolutionary algorithm performance.
  • To develop and evaluate novel families of redundant representations.

Main Methods:

  • Introduced two new families of redundant binary representations: non-neutral (linear transformations) and neutral (error control codes).
  • Assessed their impact on a (1+1)-Evolutionary Strategy (ES) using Markov chain modeling.
  • Applied the algorithm to NK fitness landscapes.

Main Results:

  • The phenotypic neighborhood created by a redundant representation strongly influences algorithm behavior, outweighing neutrality effects.
  • Higher performance on NK landscapes was observed with representations not showing extreme quality indicator values.

Conclusions:

  • Redundant representations' impact on evolutionary search is primarily mediated by the phenotypic neighborhood they induce.
  • Representation design, focusing on neighborhood structure rather than extreme neutrality, is key for effective optimization.