Genetic programming seems to be applicable in a very limited domain. Could the reason be the current state of software engineering?
What if we imagine the future of programming languages (or maybe the forgotten past), functional and homoiconic languages and better APIs (or lack thereof) in the next 20-40 years: could they enable genetic programming to finally thrive and work for common problems?
Computer scientists and engineers were thinking about universal modes of communication between computer programs since the 30s. The current state of API-driven communication is a nightmare by their standards, but we seem to only go deeper in it, solving more and more accidental complexity and losing energy and time.
LISPS continue to show us that good ideas come from simplicity, and the power of composition is the key to fighting complexity. I’d love to discuss how these ideas, if explored deeper, can allow genetic programming to evolve (pun intended) and solve real world problems at last.
I’m a professional software developer since 2010, with experience in systems programming, Windows development and web development. I’ve been working with large corporations (Ericsson), small consultancies (Macadamian Labs Canada), and startups (yvi.kz, Hexlet). I co-founded Hexlet, an educational startup in Finland. Today Hexlet is a home for 200k students who learn computer programming and software development, get hired and build cool stuff.
In 2019 I left my position as a CEO and am about to launch a new kind of online school for beginner developers.