Applications of Genetic Algorithms

My aim was to examine genetic algorithms with various parameters adjusted and explore their potential applications.

Any useful resources for it, as well as some well-known issues we may use it to tackle

This is for an investigational study. The subset cover problem is one issue that I am aware of.

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Since learning about statistical modeling, particularly with regard to Bayesian techniques, my interest in optimization has vanished. In my opinion, concentrate on more promising fields.

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When there are no good differentiable surrogate losses available and the loss landscape is not differentiable, they can be employed. Simulated annealing is also possible. Nonetheless, simulated annealing and genetic algorithms explore in different ways, so certain problems or parameterizations will do better with one than the other.

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Genetic Algorithms have numerous applications in real life including:

  • Machine learning: GAs can optimize hyperparameters in machine learning models, improving their performance and efficiency.
  • Robotics: GAs are used to evolve control systems and optimize the design of robotic components.
  • Economics: They help in modeling and solving complex economic problems, such as market simulations and resource allocation.
  • Medicine: GAs assist in optimizing treatment plans, drug formulations, and diagnostic procedures.