This chapter introduced an interesting nature-inspired algorithm family: genetic algorithms. We covered various standard optimization algorithms, varying from deterministic models, to gradient-based algorithms, to evolutionary algorithms. The biological process of evolution through natural selection was covered. We then learned how to convert our optimization problems into a form suitable for genetic algorithms. Crossover and mutation, two very crucial operations in genetic algorithms, were explained. While it is not possible to extensively cover all the crossover and mutation methods, we did learn about the popular ones.
We applied what we learned on three very different optimization problems. We used it to guess a word. The example was of a five-letter word; had we used simple brute force, it would take a search of a 615 search space. We used genetic algorithms to optimize the CNN architecture; again note that, with 19 possible bits, the search space is 219. Then, we used it to...