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Evolving Evolutionary Algorithms using Linear Genetic Programming

arXiv 2109.13110 ↗First posted 21 Aug 2021cs.NE cs.AIcited by 5

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Abstract (arXiv)

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem, and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 1 of its 44 references that are in the map. First posted on arXiv 21 Aug 2021.

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1 of 44 references.

References · 1

  1. 2015Evolving TSP heuristics using Multi Expression Programmingcited by 3

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Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 1.

5 papers.

  1. 2022A Survey of Methods for Automated Algorithm Configurationcited by 29
  2. 2023Automated Design of Metaheuristic Algorithms: A Surveycited by 12
  3. 2022AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structurescited by 4
  4. 2021New Evolutionary Computation Models and their Applications to Machine Learningcited by 0
  5. 2021Evolving Evolutionary Algorithms with Patternscited by 0