Understanding Genetic Algorithms

Genetic Algorithms in Elixir — by Sean Moriarity (10 / 101)

👈 Chapter 1 Writing Your First Genetic Algorithm | TOC | Introducing the One-Max Problem 👉

Genetic algorithms are a class of optimization algorithms based on evolution and natural selection. They use strategies loosely based on genetics and biology to produce optimal — think “best” — or near-optimal solutions to complicated problems. Initially conceived in the 1960s, the intended use for genetic algorithms was simply a technique for creating adaptable programs. Today, genetic algorithms are used in numerous applications in fields like artificial intelligence and finance. They’re great at solving difficult optimization problems and lend themselves nicely to parallel computing and distributed architectures. They can even yield solutions to the shipping problem mentioned earlier.

The First Genetic Algorithm

INFORMATION

The first genetic algorithm was introduced by John Holland at the University of Michigan in the 1960s; however, evolutionary algorithms had been around long before that. Early artificial intelligence researchers believed evolution was the key to creating truly intelligent programs. Today, the field of evolutionary computation has many, somewhat loosely defined, branches of research, such as evolution strategies, genetic programming, and genetic algorithms.