-
Book Overview & Buying
-
Table Of Contents
Hands-On Artificial Intelligence for IoT - Second Edition
By :
This chapter introduced an interesting nature-inspired algorithm family: GAs. We covered various standard optimization algorithms, varying from deterministic models, to gradient-based algorithms, to EAs. The biological process of evolution through natural selection was covered. We then learned how to convert our optimization problems into a form suitable for GAs. Crossover and mutation, two very crucial operations in GAs, 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 to 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 GAs to optimize the CNN architecture; again, note that, with 19 possible bits, the search space is 219. Then, we used it to find the optimum hyperparameters for an LSTM network.
In the next...