genetic-algorithm

I have studied about Non dominating sorting algorithtm (nsga-II). Algorithm is given on this link . http://church.cs.virginia.edu/genprog/images/2/2f/Nsga_ii.pdf I want to know it's real life application with examples.....I tried to search on the internet ,but no where found it. If you have any ideas or relevent data/link ,please share with me. You can find some real-life applications by just searching : "NSGA-II + applications" in Google Scholar : http://scholar.google.com/scholar?start=10&q=nsga-ii+application&hl=en&as_sdt=0,5 The ones who proposed NSGA-II are, indeed, Prof. Kalyanmoy Deb

i want to see application of GA in image processing. I was not getting how to write the fitness function for any image. Say I have a image in grey scale,which is not very clear, now if I keep matching my generated solution with this picture value then how is my solution going to be better like better clearity increased contrast/brightness etc. My favorite example of Genetic Algorithms in image processing is: http://rogeralsing.com/2008/12/07/genetic-programming-evolution-of-mona-lisa/ It evolves random shapes into something that resembles the Mona Lisa. You can actually try it and play with it

I am trying to build a 4 x 4 sudoku solver by using the genetic algorithm. I have some issues with values converging to local minima. I am using a ranked approach and removing the bottom two ranked answer possibilities and replacing them with a crossover between the two highest ranked answer possibilities. For additional help avoiding local mininma, I am also using mutation. If an answer is not determined within a specific amount of generation, my population is filled with completely new and random state values. However, my algorithm seems to get stuck in local minima. As a fitness function, I

Some Genetic Algorithm frameworks, such as http://www.aforgenet.com/ requires many parameters, such as mutation rate, population size, etc There is universal best numbers for such parameters? I believe that it depends on the problem (fitness function delay, mutation delay, recombination delay, evolution rate, etc). My first thought was to use a GA to configure another GA. Any better ideas? The one time I programmed a genetic algorithm I included those values in the values to mutate, basically like you said using a GA to configure itself. It worked surprisingly well, especially since I've found

In a genetic algorithm, when selecting members for crossover using roulette-wheel selection method, does the population first need to be sorted by fitness rank? The possibilities seem to be: sort population first by ascending fitness sort population by descending fitness don't sort population & let the roulette ball fall where it may.. I'm thinking that sorting either way may have no effect - a pebble landing at random on a wheel containing different sized (by fitness) slices will have exactly the same outcome chance whether the larger slices are grouped together or not. But I'm not 100%

I decided to go with a Neural Network in order to create behaviors for an animation engine that I have. The neural network takes in 3 vector3s and 1 Euler angle for every body part that I have. The first vector3 is the position, the second is its velocity, and the third is its angular velocity. The Euler angle is what rotation the body part is at. and I have 7 body parts. Each one of those data types has 3 floats. 7*4*3 = 84, so I have 84 inputs for my neural network. The outputs are mapped to the muscles of the character. They provide the amount of strength to apply to each muscle, and there