Optimizing Continuous Functions

This chapter describes how continuous search-space optimization problems can be solved by genetic algorithms. We will start by describing the chromosomes and genetic operators commonly used for genetic algorithms with real number-based populations and go over the tools offered by the Distributed Evolutionary Algorithms in Python (DEAP) framework for this domain. We will then cover several hands-on examples of continuous function optimization problems and their Python-based solutions using the DEAP framework. These include the optimization of the Eggholder function, Himmelblau’s function, as well as the constrained optimization of Simionescu’s function. Along the way, we will learn about finding multiple solutions using niching and sharing and handling constraints.

By the end of this chapter, you will be able to do the following:

• Understand chromosomes and genetic operators used for real numbers
• Use DEAP to optimize continuous...