front matter
When I started my career in machine learning and artificial intelligence 25+ years ago, two dominant technologies were considered the next big things. Both technologies showed promise in solving complex problems and both were computationally equivalent. Those two technologies were evolutionary algorithms and neural networks (deep learning).
Over the next couple of decades, I witnessed the steep decline of evolutionary algorithms and explosive growth of deep learning. While this battle was fought and won through computational efficiency, deep learning has also showcased numerous novel applications. On the other hand, for the most part, knowledge and use of evolutionary and genetic algorithms dwindled to a footnote.
My intention for this book is to demonstrate the capability of evolutionary and genetic algorithms to provide benefits to deep learning systems. These benefits are especially relevant as the age of DL matures into the AutoML era, in which being able to automate the large and wide-scale development of models is becoming mainstream.
I also believe that our search for generalized AI and intelligence can be assisted by looking at evolution. After all, evolution is a tool nature has used to form our intelligence, so why can’t it improve artificial intelligence? My guess is we are too impatient and arrogant to think humanity can solve this problem on its own.