Self-adaptive learning for hybrid genetic algorithms

Tarek A. El-Mihoub, Adrian A. Hopgood, Lars Nolle

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Abstract

Local search can be introduced into genetic algorithms to create a hybrid, but any improvement in performance is dependent on the learning mechanism. In the Lamarckian model, a candidate solution is replaced by a fitter neighbour if one is found by local search. In the Baldwinian model, the original solution is retained but with an upgraded fitness if a fitter solution is found in the local search space. The effectiveness of using either model or a variable proportion of the two within a hybrid genetic algorithm is affected by the topology of the fitness function and the details of the hybrid algorithm. This paper investigates an intelligent adaptive approach to decide on the learning mechanism to be used by an individual over the course of the search. Evolution is used to self-adapt both the frequency of a steepest-descent local search and the relative proportions of Lamarckian and Baldwinian inheritance. Experiments have shown that this form of adaptive learning can improve the ability to find high-quality solutions and can accelerate the hybrid search without the need to find optimal control parameters for the learning process.
Original languageEnglish
Pages (from-to)1565–1579
Number of pages15
JournalEvolutionary Intelligence
Volume14
Issue number4
Early online date25 May 2020
DOIs
Publication statusPublished - 25 Oct 2021

Keywords

  • Hybrid genetic algorithms
  • Evolution strategies
  • Learning strategies
  • Self-adaptive learning
  • Reinforcement learning
  • Memetic algorithms
  • Metaheuristics
  • Baldwinism
  • Lamarckism

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