A Comprehensive Guide to Genetic Algorithms (and how to code them)
On the Origin of Genetic Algorithms
Original article by Rishabh Anand
In the mid 19th century, Charles Darwin postulated the theory of evolution and how it played a key role in enabling organisms to adapt to their environments through natural selection – a process where the fittest in a given population survive and live long enough to pass on their traits and characteristics to future generations to ensure their survival.
What’s happening currently?
Presently, Machine Learning (ML) has kicked off a new era of smarter machines capable of making better decisions compared to their rule-based counterparts from the late 90’s and early 2000’s.
Harnessing the sheer amount of computational power we now possess, ML algorithms, specifically deep neural networks, have leveraged our large pools of data, both structured and unstructured, to deliver insights, leads, predictions and much more with a high degree of accuracy.
State-of-the-art ML models can now classify, categorise, and generate data from scratch with a few hours/days of training. Deep Neural Networks have now proliferated into multiple domains, now being able to work with different data formats ranging from images…
