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Evolving evolutionary algorithms using linear genetic programming.

Mihai Oltean1

  • 1Department of Computer Science, Babeş-Bolyai University, Kogalniceanu 1, Cluj-Napoca 3400, Romania. moltean@cs.ubbcluj.ro

Evolutionary Computation
|September 15, 2005
PubMed
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This study introduces a novel model for evolving Evolutionary Algorithms (EAs) using Linear Genetic Programming (LGP). Evolved EAs demonstrate competitive or superior performance on benchmark optimization problems compared to standard methods.

Area of Science:

  • Computational Intelligence
  • Evolutionary Computation
  • Machine Learning

Background:

  • Evolutionary Algorithms (EAs) are powerful optimization tools.
  • Designing effective EAs often requires expert knowledge and extensive tuning.
  • Linear Genetic Programming (LGP) offers a framework for evolving programs.

Purpose of the Study:

  • To propose a new model for automatically evolving Evolutionary Algorithms.
  • To leverage Linear Genetic Programming (LGP) for EA development.
  • To assess the performance of evolved EAs on complex problems.

Main Methods:

  • A novel model based on Linear Genetic Programming (LGP) was developed.
  • Each LGP chromosome encodes a complete Evolutionary Algorithm.

Related Experiment Videos

  • The model was used to evolve EAs for function optimization, Traveling Salesman Problem, and Quadratic Assignment Problem.

    • Main Results:

    • Evolved EAs were generated using the proposed LGP-based model.
    • Numerical experiments were conducted on standard benchmarking problems.
    • The evolved EAs achieved performance comparable to, and in some cases exceeding, standard approaches.

    Conclusions:

    • The proposed LGP-based model is effective for evolving high-performing Evolutionary Algorithms.
    • This approach automates EA design, potentially reducing manual effort.
    • The evolved EAs show promise for various optimization tasks.