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A review on genetic algorithm: past, present, and future.

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Summary
This summary is machine-generated.

This review analyzes recent advances in genetic algorithms (GAs), covering popular algorithms, operators, and applications. It aims to provide researchers with a comprehensive overview and insights into future research directions in GAs.

Keywords:
CrossoverEvolutionGenetic algorithmMetaheuristicMutationOptimizationSelection

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Area of Science:

  • Computational Intelligence
  • Evolutionary Computation
  • Artificial Intelligence

Background:

  • Genetic algorithms (GAs) are powerful optimization and search techniques inspired by natural evolution.
  • Recent advancements have expanded their applicability across diverse scientific domains.
  • A structured overview is needed to guide new researchers in this rapidly evolving field.

Purpose of the Study:

  • To review and analyze recent significant advances in genetic algorithms.
  • To provide a comprehensive vision of GAs for researchers and graduate students.
  • To highlight key algorithms, operators, and application areas.

Main Methods:

  • Selection of prominent genetic algorithms based on research community interest.
  • Analysis of well-known GA implementations, including their advantages and disadvantages.
  • Discussion of genetic operators, their functionalities, and applications.
  • Exploration of various research domains utilizing genetic algorithms.

Main Results:

  • Identification of key genetic algorithms and their practical implementations.
  • Detailed explanation of genetic operators and their strategic use.
  • Overview of the diverse application landscape of genetic algorithms.
  • Summary of the strengths and weaknesses of different GA approaches.

Conclusions:

  • The review offers a structured perspective on genetic algorithms, beneficial for both novice and experienced researchers.
  • Future research directions include advancements in genetic operators, fitness function design, and hybrid algorithmic approaches.
  • This work serves as a valuable resource for academic research and graduate-level teaching in evolutionary computation.