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AutoMH: Automatically Create Evolutionary Metaheuristic Algorithms Using Reinforcement Learning.

Boris Almonacid1

  • 1Global Change Science, Puerto Varas 5550000, Chile.

Entropy (Basel, Switzerland)
|July 27, 2022
PubMed
Summary

This study introduces an automated method for creating metaheuristic algorithms using evolutionary processes and reinforcement learning. The approach successfully generated a novel algorithm capable of solving complex optimization problems.

Keywords:
evolutionary metaheuristichigh-level data driven metaheuristicsmachine learningmetaheuristicmetaheuristic generationonline learningoptimisationreinforcement learningsearch trajectory networks

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization

Background:

  • Machine learning effectively solves diverse problems, with optimization being a key research area.
  • Metaheuristic algorithms offer efficient solutions for optimization problems but require significant time for appropriate selection and configuration.
  • Automating the creation of tailored metaheuristic algorithms is crucial for advancing optimization research.

Purpose of the Study:

  • To develop an approach for automatically generating metaheuristic algorithms tailored to specific optimization problems.
  • To leverage evolutionary processes and multi-agent reinforcement learning for algorithm creation.
  • To establish a foundation for online-evolutionary generation of metaheuristic algorithms.

Main Methods:

  • An evolutionary process modifies the logical structure of metaheuristic algorithms.
  • A multi-agent reinforcement learning framework guides the evolutionary process.
  • A learning agent comprising analysis and modification components drives algorithm development.

Main Results:

  • The proposed approach successfully created a metaheuristic algorithm.
  • The generated algorithm demonstrated proficiency in solving various continuous domain optimization problems.
  • Experimental validation confirmed the efficacy of the automated generation method.

Conclusions:

  • The automated generation of metaheuristic algorithms is feasible and effective.
  • This work provides a basis for online-evolutionary development of optimization algorithms.
  • The approach has immediate implications for advancing machine learning and optimization research.