4 More evolutionary computation with DEAP

 

This chapter covers

  • Developing regression solvers with genetic programming in DEAP
  • Applying particle swarm optimization to solve for unknowns in complex functions
  • Breaking a problem into components and coevolving a solution
  • Understanding and applying evolutionary strategies to approximating solutions
  • Approximating continuous and discontinuous solutions with differentiable evolution

In chapter 3, we just started to scratch the surface of what evolutionary computation looks like by introducing GAs. Starting with GA helps us set several foundations we continue to develop in this chapter. We also continue our progression from GA by exploring other evolutionary search methods for solving more specialized and complex problems. In this chapter, we look at other forms of the evolutionary search for solving a wider variety of problems.

There are a wide variety and forms of evolutionary algorithms, each with different strengths and weaknesses. Understanding other available options strengthens our understanding of where to apply which algorithm. As seen in this chapter, there is more than one way to peel an orange.

4.1 Genetic programming with DEAP

4.1.1 Solving regression with genetic programming

4.1.2 Learning exercises

4.2 Particle swarm optimization with DEAP

4.2.1 Solving equations with PSO

4.2.2 Learning exercises

4.3 Coevolving solutions with DEAP

4.3.1 Coevolving genetic programming with genetic algorithms

4.4 Evolutionary strategies with DEAP

4.4.1 Applying evolutionary strategies to function approximation

4.4.2 Revisiting the EvoLisa

4.4.3 Learning exercises

4.5 Differential evolution with DEAP

4.5.1 Approximating complex and discontinuous functions with DE