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Estimation of Distribution Algorithm for Grammar-Guided Genetic Programming.

Pablo Ramos Criado1, D Barrios Rolanía2, David de la Hoz3

  • 1Aturing Research, Salamanca, Spain pablo.ramos@aturing.com.

Evolutionary Computation
|January 25, 2024
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Summary
This summary is machine-generated.

This study introduces a smoothed estimation of distribution algorithm (SEDA) for grammar-guided genetic programming. SEDA enhances evolutionary algorithms by balancing exploration and local search for improved optimization performance.

Keywords:
Grammar-guided genetic programmingestimation of distribution algorithmsgenetic variation operatorslocal search, localitysearch-space exploration

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Computational Intelligence

Background:

  • Genetic variation operators in grammar-guided genetic programming (GGP) face challenges in balancing exploration and local search.
  • This limitation can hinder the efficiency of evolutionary algorithms in solving complex search and optimization problems.

Purpose of the Study:

  • To introduce a novel algorithm, the smoothed estimation of distribution algorithm (SEDA), for grammar-guided genetic programming.
  • To address the exploration-exploitation trade-off limitations inherent in traditional genetic programming variation operators.

Main Methods:

  • Developed an estimation of distribution algorithm (EDA) tailored for GGP.
  • Employed an extended dynamic stochastic context-free grammar to model the search space distribution from promising individuals.
  • Introduced a smoothing technique to the estimated distribution model to enhance exploratory behavior, defining the SEDA approach.

Main Results:

  • Compared SEDA against a standard genetic programming crossover operator and an incremental EDA on challenging problems.
  • SEDA demonstrated superior performance in achieving accurate solutions.
  • The proposed SEDA achieved accuracy comparable to other methods while maintaining an intermediate convergence speed.

Conclusions:

  • The smoothed estimation of distribution algorithm (SEDA) effectively improves the performance of grammar-guided genetic programming.
  • SEDA offers a balanced approach to exploration and local search, leading to more accurate solutions in optimization tasks.
  • This research provides a valuable advancement for evolutionary computation and search optimization methodologies.