Hands-On Genetic Algorithms with Python

The genetic algorithms analogy

Genetic algorithms seek to find the optimal solution for a given problem. Whereas Darwinian evolution maintains a population of individual specimens, genetic algorithms maintain a population of candidate solutions, called individuals, for that given problem. These candidate solutions are iteratively evaluated and used to create a new generation of solutions. Those who are better at solving this problem have a greater chance of being selected and passing their qualities to the next generation of candidate solutions. This way, as generations go by, candidate solutions get better at solving the problem at hand.

In the following sections, we will describe the various components of genetic algorithms that enable ...