Genetic programming

What Hacker News says

1. eggyMay 2023
   I was studying evolutionary computation back in the 80s. Yes, you did not have the large ANNs you have today, but you also broke down your problem into manageable, computable bits (no pun intended). Working through Koza's book on Genetic Programming in the 90s, or Mark Watson's book on ANNS, chaos theory, and a bunch of other stuff, was very illuminating. I particularly liked the artificial life stuff out of the Santa Fe Institute. I still have a VHS from the 2nd proceedings. I could have rode the ML/DL wave to a high salary job, but I took another path building real-world objects and machines, and I am very happy I did. I remember when WordPerfect was popular and people spoke of careers in wordprocessing. I always saw it as evolution of the cube farm, and of course, it is. Now I have friends who work in industry with ML/DL, and they joke that it's the same but with a lot more things to track and keep running. I like GOFAI for the simple matter that sometimes 90% is good enough, and 125% is a waste of time, water in the blender. When robotics was taking off I saw the two schools of thought: Rodney Brooke's subsumption architecture making it simple to create emergent behavior, and Mark Tilden's analog circuits and discrete components. I found Tilden's work more interesting. It spoke to me about continuous or biological-like processes. I am now into researching neurocomputing and following the developments in designing neurocomputing hardware. I was so happy to have received free tickets to watch Terminator 2 back in the day, but now I feel like I have a front-row seat to it!
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2. criddellApr 2023
   Somehow I got a copy of James Gleick's book Chaos and it had a big impact on me. I was kind of obsessed with chaos and fractals for a while. Right around that time a whole bunch of similar pop sci books came out and I read a bunch of them. Steven Levy's Artificial Life, a Mondo 2000 collection, John Koza's Genetic Programming, and Cliff Stoll's The Cuckoo's Egg all stand out in my memory.
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3. eggySep 2022
   1. Amiga Assembly Language Programming (1987) - The book that tore me away from the Commodore BASIC I learned on my PET 2001 in 1978.

   2. The Architecture of Symbolic Computers - (1991) - I didn't read this until the 2000s, but boy, what a book! It's about Lisp and Lisp Machines, but it covers everything that is still applicable to computers. A great example of a dense, readable textbook compared to all the fluff written nowadays with too many pictures and repeated explanations.

   3. The C Programming Language, 2nd Edition - you know what I am talking about!

   4. Genetic Programming (1992)- John Koza - Really got me into early machine learning and more into LISP!

   5. The Handbook of Neuroevolution Through Erlang (2013) - Gene Sher - Great intro to ANNs in general, and Erlang even if you don't program in Erlang or want to!

   Honorable mentions:

   A. The Book of Shen - Mark Tarver - It's a LISP, but the history of computing and Lambda Calculus is nice, and Shen is an amazing language.

   B. Building High Integrity Applications with SPARK 1st Edition - Great intro to SPARK, and Ada subset, used in the book for the CubeSAT program at VTC (Vermont Technical College).

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4. eprJun 2019
   You may want to look into symbolic regression. I often find that by applying a downward pressure on the fitness of less parsimonious individuals as well as one or more substitution operators (where applicable) you can get very elegant results. Add constraints or other fitness bonuses for specific types of relationships you are looking for and you might be surprised by what cool things you can find. Also a great excuse to learn Common Lisp if you haven't already.

   A good starting point is John Koza's first book on Genetic Programming.

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5. unotiJun 2015
   The mating process looks like it is a mathemetical average of various statistics of the mother and father. It wasn't the kind of cool dna I envisioned, where the food search algorithm is a little program that the creatures execute.

   If you're interested in writing software that genetically writes other software, check out this amazing book by Koza[1]. It's one of my favorite books that I have on my shelf. Armed with this book and a computer, you can have a ton of fun.

   In that book, the author dedicates a chapter to virtual ants, and evolves programs for the ants that control where they move and what search strategies they use based on sensor inputs. And that's by far not the most interesting type of program he evolves. It's a thick book, and there are things like evolving a program to drive a tractor trailer and back it into a loading dock.

   As a little bonus, this book was how I ended up understanding the vital importance of AST's, and it's how I first "groked" LISP. LISP isn't a pre-requisite to understanding the book, but you'll understand the big concepts by the end.

   [1] http://www.amazon.com/Genetic-Programming-Computers-Selectio...

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6. unotiJan 2011
   I bought this book a few years ago, by Koza. It has tons of working code in it-- each problem he steps through building up a solution with a working program:

   http://www.amazon.com/Genetic-Programming-Computers-Selectio...

   This is one of my most prized computer books, perhaps the most prized book I have. Even though I wasn't a Lisper when I started reading this book, I ended up building (without realizing it at first) a little Lisp inside Python for exploring the concepts.

   If you thought Koza's old website is inspiring, consider getting this book. It's huge and expensive, but to me it feels like one of the 3 books I'd want to bring with me to a desert island or through the destruction of civilization.

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