Main page - Introduction to Genetic Algorithms - Tutorial with Interactive Java Applets
http://www.obitko.com/tutorials/genetic-algorithms/index.php

e area of genetic algorithms is very wide, it is not possible to cover everything in these pages. But you should get some idea, what the genetic algorithms are and what they could be useful for. Do not expect any sophisticated mathematic

Pyevolve was developed to be a complete genetic algorithm framework written in pure python.

Clojure is surrounded by hype these days. The word on the streets is that Clojure is the Next Big Thing. It has access to the largest library of code and it proposes a nice solution the to the concurrency problem. Lots more has been said... But I haven't seen a lot of code. So I set out to make a small but meaningful program in Clojure to get a sense of it's potential. I give Clojure two thumbs up, and I think you'll do too. The Mona Lisa Problem The program I present tries to paint Mona Lisa with a small number of semi-transparent colored polygons. It does so by using Darwin's theory of evolution to evolve programs that draw Mona Lisa.

The (clojure) program I present tries to paint Mona Lisa with a small number of semi-transparent colored polygons. It does so by using Darwin's theory of evolution to evolve programs that draw Mona Lisa.

Wow.

service that effectively combines the theory of natural selection with banner ads - with very impressive results.

Weebly product that optimizes ads based on genetic algorithms.

In 1981, Eurisko, a computer program that arguably displays the rudiments of such skills, easily won the Traveller tournament, becoming the top-ranked player in the United States and an honorary Admiral in the Traveller navy. Eurisko had designed its fleet according to principles it discovered itself–with some help from its inventor, Douglas B. Lenat, an assistant professor in Stanford University’s artificial-intelligence program.

popular overview

Trillion Credit Squadron playing 'bot in 1984. Cool.

"During one run, Lenat noticed that the number in the Worth slot of one newly discovered heuristic kept rising, indicating that Eurisko had made a particularly valuable find. As it turned out the heuristic performed no useful function. It simply examined the pool of new concepts, located those with the highest Worth values, and inserted its name in their My Creator slots. It was a heuristic that, in effect, had learned how to cheat." :via the new yorker, 2009.05.11 :via http://en.wikipedia.org/wiki/Eurisko#cite_note-newyorker-2

"In 1981, Eurisko, a computer program that arguably displays the rudiments of such skills, easily won the Traveller tournament, becoming the top-ranked player in the United States and an honorary Admiral in the Traveller navy. Eurisko had designed its fleet according to principles it discovered itself–with some help from its inventor, Douglas B. Lenat, an assistant professor in Stanford University’s artificial-intelligence program."

A good idea of image compression based on genetic algorithms.

Some of the most useful applications of genetic algorithms in the real world.

Although the configuration program specified tasks for all 100 cells, it transpired that only 32 were essential to the circuit's operation. Thompson could bypass the other cells without affecting it. A further five cells appeared to serve no logical purpose at all--there was no route of connections by which they could influence the output. And yet if he disconnected them, the circuit stopped working.

genetic fpga evolution/programming

CREATURES FROM PRIMORDIAL SILICON

apply evolution to digital FPGA

Using FPGAs to evolve solutions to problems.

Genetic algorithms with Python

genetic algorithm lib use