Chapter 13. Evolutionary Computing
There's a lot more to evolutionary computing than genetic algorithms. The first foray into evolutionary computing was motivated by the need to address different types of large combinatorial problems also known as NP problems. This field of research was pioneered by John Holland [10:1] and David Goldberg [10:2] to leverage the theory of evolution and biology to solve combinatorial problems. Their findings should be of interest to anyone eager to learn about the foundation of genetic algorithms (GA) and artificial life.
This chapter covers the following topics:
The origin of evolutionary computing
The purpose and foundation of genetic algorithms as well as their benefits and limitations
From a practical perspective, you will learn how to:
Apply genetic algorithms to leverage a technical analysis of market price and volume movement to predict future returns
Evaluate or estimate the search space
Encode solutions in the binary format using either hierarchical or flat...