Evolutionary Deep Learning

3 Introducing genetic algorithms with DEAP

This chapter covers

• Creating genetic solvers using DEAP
• Applying GA to a complex design or placement problem
• Solving or estimating mathematically difficult problems with GA
• Determining which GA operators to employ when solving problems
• Constructing complex gene structures for design and drawing

In the last chapter, we explored the origins of life simulation and how evolution and natural selection can be used for optimization. We learned how genetic algorithms, a subset of evolutionary computation, could extend these concepts further into an elegant practical method of optimized search.

In this chapter, we directly extend what we learned in the last chapter to tackle larger and more complex problems ...