Ideas from books, articles & podcasts.
Inspiration: “Using the biological evolution as a source of inspiration, evolutionary computation (EC) solves optimization problems by generating, evaluating and modifying a population of possible solutions.” 
Genetic Algorithms (GAs) are likely the most popular algorithm belonging to EC. Particle Swarm Optimization, Ant Colony Optimization, Genetic Programming (among others) also belong to evolutionary computation, but we’ll limit the scope to GAs.
MORE IDEAS FROM THE SAME ARTICLE
Inspiration: “Using the human brain as a source of inspiration, artificial neural networks (NNs) are massively parallel distributed networks that have the ability to learn and generalize from examples.” 
AI is ubiquitous. The term has flooded our lives, and it has lost its flavor. But, as a data scientist / ML engineer / AI engineer (whatever you call yourself), we can hold the community to a higher standard. We can be specific with our algorithms, so showcase our work is more than a series of pr...
One of the most common questions I’ve received when talking about CI is, “what problems does each branch solve?” While I can appreciate this question, the branches are not segmented by which problems they solve.
What is Artificial Intelligence? Who knows. It’ s an ever-moving target to define what is or isn’t AI. So, I’d like to dive into a science that’s a little more concrete — Computational Intelligence (CI). CI is a three-branched set of theories along with their design a...
Inspiration: “Using the human language as a source of inspiration, fuzzy systems (FS) model linguistic imprecision and solve uncertain problems based on a generalization of traditional logic, which enables us to perform approximate reasoning.” 
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