Key Takeaways

  • Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution.
  • Genetic algorithms can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics.
  • Genetic algorithms can be used to improve machine learning and deep learning models in several different ways, such as feature selection, hyperparameter tuning and architecture optimization.
  • Genetic algorithms can be utilized for reinforcement learning by optimizing the cumulative reward sought after.
  • Genetic Programming is a special case of Genetic Algorithms, that can be used to generate computer programs with desired functionality.
  • There are numerous other bio-inspired algorithms that can solve problems using biological models and behaviors.
Hands-On Genetic Algorithms with Python by Eyal Wirsansky is a new book which explores the world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models. InfoQ interviewed Eyal Wirsansky about how genetic algorithms work and what they can be used for.