In evolution strategies, the individuals are coded as vectors of real numbers. On reproduction, parents are selected randomly and the fittest offsprings are selected and inserted in the next generation. ES individuals are self-adapting. The step size or “mutation strength” is encoded in the individual, so good parameters get to the next generation by selecting … Read more
If you are sure you want to do this, you want genetic programming, rather than a genetic algorithm. GP allows you to evolve tree-structured programs. What you would do would be to give it a bunch of primitive operations (while($register), read($register), increment($register), decrement($register), divide($result $numerator $denominator), print, progn2 (this is GP speak for “execute two … Read more
Although I’m not sure how to translate the following algorithm into GA (and I’m not sure why you need to use GA for this problem), and I could be off base in proposing it, here goes. The simple technique I would propose is to count the number of black pixels per row. (Actually it’s the … Read more
Genetic algorithms (GA) are search algorithms that mimic the process of natural evolution, where each individual is a candidate solution: individuals are generally “raw data” (in whatever encoding format has been defined). Genetic programming (GP) is considered a special case of GA, where each individual is a computer program (not just “raw data”). GP explore … Read more
Genetic Algorithms have been widely used commercially. Optimizing train routing was an early application. More recently fighter planes have used GAs to optimize wing designs. I have used GAs extensively at work to generate solutions to problems that have an extremely large search space. Many problems are unlikely to benefit from GAs. I disagree with … Read more
It’s been a few years since i’ve done this myself, however the following pseudo code was found easily enough on google. for all members of population sum += fitness of this individual end for for all members of population probability = sum of probabilities + (fitness / sum) sum of probabilities += probability end for … Read more
Best references for me so far: Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg: a classic, still considered as the bible of GAs by many. An Introduction to Genetic Algorithms by Melanie Mitchell: more recent than the previous reference and packed with probably more interesting examples. A Field Guide to Genetic … Read more
I built an evolutionary algorithm for retail inventory replenishment in a product targeted at huge plant nurseries (and there are some really big, smart ones — $200m companies). It was probably the coolest thing I’ve ever worked on. Using three years of historical data, it crunched and evolved for a week straight while I was … Read more
Disclosure: I am of one of the developers of DEAP. DEAP is the most actively developed project amongst the ones mentioned. It has an active mailing-list, which is an interesting feature if you need help at some point. The class creation which is unique to DEAP makes switching from single to multiple objectives really easy. … Read more
One topic with lots of possibilities is to use evolutionary algorithms to evolve strategies for game playing. People have used evolution to generate strategies for poker, checkers/draughts, Go and many other games. The J-GAP people have used genetic programming to evolve bots for Robocode. I recently posted an introductory article about evolutionary computation. It includes … Read more