Throughout this book, we have covered the problem-solving technique known as genetic algorithms and applied it to numerous types of problems, including combinatorial optimization, constraint satisfaction, and continuous function optimization, as well as to machine learning and artificial intelligence. However, as we mentioned in Chapter 1, An Introduction to Genetic Algorithms, genetic algorithms are just one branch within a larger family of algorithms called evolutionary computation. This family consists of various related problem-solving and optimization techniques, all of which draw inspiration from Charles Darwin's theory of natural evolution.
The main characteristics that are shared by these techniques are as follows:
- The starting point is an initial set (population) of candidate solutions.
- The candidate solutions...
