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Related Experiment Video

Updated: May 27, 2026

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
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Estimating meme fitness in adaptive memetic algorithms for combinatorial problems.

J E Smith1

  • 1Department of Computer Science and Creative Technologies, University of the West of England, UK. james.smith@uwe.ac.uk

Evolutionary Computation
|December 2, 2011
PubMed
Summary

Adaptive memetic algorithms (AMAs) use reward schemes for local search operators. Mean improvement rewards outperform extreme rewards, and local schemes are better than global ones for combinatorial problems.

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Published on: October 11, 2018

Area of Science:

  • Heuristic optimization
  • Artificial intelligence
  • Computational intelligence

Background:

  • Adaptive memetic algorithms (AMAs) are a promising research area in heuristic optimization.
  • AMAs adapt local improvement operator probabilities based on their estimated value to the search process.
  • Estimating the value of these operators is crucial for AMA performance.

Purpose of the Study:

  • To investigate how the current value of local improvement operators in AMAs should be estimated.
  • To compare reward schemes based on mean versus extreme improvements.
  • To evaluate the effectiveness of global versus local reward aggregation in combinatorial spaces.

Main Methods:

  • Utilized the COMA framework for coevolving memes and candidate solutions.
  • Implemented two distinct AMAs: one with adaptive operator pursuit and another with dynamic meme adaptation.
  • Conducted experiments on binary encoded combinatorial problems with varying numbers of variables.

Main Results:

  • Both investigated AMA methods proved highly effective.
  • Reward schemes based on mean improvement consistently outperformed those based on extreme improvement.
  • Local reward schemes demonstrated superior performance over global schemes in combinatorial search spaces.

Conclusions:

  • Mean improvement-based reward schemes are more effective for AMAs in combinatorial optimization.
  • Local reward schemes are better suited for combinatorial spaces compared to continuous spaces.
  • AMA performance is significantly influenced by the choice of reward estimation strategy.