What are good examples of genetic algorithms/genetic programming solutions? [closed]

Answer 1

A couple of weeks ago, I suggested a solution on SO using genetic algorithms to solve a problem of graph layout. It is an example of a constrained optimization problem.

Also in the area of machine learning, I implemented a GA-based classification rules framework in c/c++ from scratch.
I've also used GA in a sample project for training artificial neural networks (ANN) as opposed to using the famous backpropagation algorithm.

In addition, and as part of my graduate research, I've used GA in training Hidden Markov Models as an additional approach to the EM-based Baum-Welch algorithm (in c/c++ again).

Answer 2

in undergrad, we used NERO (a combination of neural network and genetic algorithm) to teach in-game robots to make intelligent decisions. It was pretty cool.

Answer 3

After reading The Blind Watchmaker, I was interested in the pascal program Dawkins said he had developed to create models of organisms that could evolve over time. I was interested enough to write my own using Swarm. I didn't make all the fancy critter graphics he did, but my 'chromosomes' controlled traits which affected organisms ability to survive. They lived in a simple world and could slug it out against each other and their environment.

Organisms lived or died partly due to chance, but also based on how effectively they adapted to their local environments, how well they consumed nutrients & how successfully they reproduced. It was fun, but also more proof to my wife that I am a geek.

Answer 4

As well as some of the common problems, like the Travelling Salesman and a variation on Roger Alsing's Mona Lisa program, I've also written an evolutionary Sudoku solver (which required a bit more original thought on my part, rather than just re-implementing somebody else's idea). There are more reliable algorithms for solving Sudokus but the evolutionary approach works fairly well.

In the last few days I've been playing around with an evolutionary program to find "cold decks" for poker after seeing this article on Reddit. It's not quite satisfactory at the moment but I think I can improve it.

I have my own framework that I use for evolutionary algorithms.

Answer 5

I developed a home brew GA for a 3D laser surface profile system my company developed for the freight industry back in 1992. The system relied upon 3 dimensional triangulation and used a custom laser line scanner, a 512x512 camera (with custom capture hw). The distance between the camera and laser was never going to be precise and the focal point of the cameras were not to be found in the 256,256 position that you expected it to be!

It was a nightmare to try and work out the calibration parameters using standard geometry and simulated annealing style equation solving.

The Genetic algorithm was whipped up in an evening and I created a calibration cube to test it on. I knew the cube dimensions to high accuracy and thus the idea was that my GA could evolve a set of custom triangulation parameters for each scanning unit that would overcome production variations.

The trick worked a treat. I was flabbergasted to say the least! Within around 10 generations my 'virtual' cube (generated from the raw scan and recreated from the calibration parameters) actually looked like a cube! After around 50 generations I had the calibration I needed.

Answer 6

I once used a GA to optimize a hash function for memory addresses. The addresses were 4K or 8K page sizes, so they showed some predictability in the bit pattern of the address (least significant bits all zero; middle bits incrementing regularly, etc.) The original hash function was "chunky" - it tended to cluster hits on every third hash bucket. The improved algorithm had a nearly perfect distribution.