Genetic Algorithms Revisited

We used Genetic Algorithms a few times,  but turned out it was rarely useful for the kind of marketing and supply chain problems we encountered.    Often because the problems contained much metadata complexity that was hard to incorporate.   GA's are a way to simulate problems to get less than optimal solutions, when you can readily state the problem parameters.    Note the mention below that GA's have become a way to look for optimal 'hyperperameters' used in machine learning methods.   So they are back in vogue.

Tags: Algorithms, Evolutionary Algorithm, Explained, Genetic Algorithm, Key Terms, Optimization

Genetic Algorithms.  Terms and Motivation   By Matthew Mayo, KDnuggets.

Genetic algorithms, inspired by natural selection, are a commonly used approach to approximating solutions to optimization and search problems. Their necessity lies in the fact that there exist problems which are too computationally complex to solve in any acceptable (or determinant) amount of time.

Take the well-known travelling salesman problem, for example. As the number of cities involved in the problem grow, the time required for determining a solution quickly becomes unmanageable. Solving the problem for 5 cities, for example, is a trivial task; solving it for 50, on the other hand, would take an amount of time so unreasonable as to never complete.

It turns out that approximating such optimization problems with genetic algorithms is a sensible approach, resulting in reasonable approximations. Genetic algorithms have had a place in the machine learning repertoire for decades, but their recent revival as tools for optimizing machine learning hyperparameters (and traversing neural network architecture search spaces) has brought them to the attention of a new generation of machine learning researchers and practitioners.

This article presents simple definitions for 12 genetic algorithm key terms, in order to help better introduce the concepts to newcomers. .... "