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.
