evolutionary-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

Can someone please explain the Differential Evolution method? The Wikipedia definition is extremely technical. A dumbed-down explanation followed by a simple example would be appreciated :) Here's a simplified description. DE is an optimisation technique which iteratively modifies a population of candidate solutions to make it converge to an optimum of your function. You first initialise your candidate solutions randomly. Then at each iteration and for each candidate solution x you do the following: you produce a trial vector: v = a + ( b - c ) / 2, where a, b, c are three distinct candidate

I want to upgrade my evolution simulator to use Hebb learning, like this one . I basically want small creatures to be able to learn how to find food. I achieved that with the basic feedforward networks, but I'm stuck at understanding how to do it with Hebb learning. The basic principle of Hebb learning is that, if two neurons fire together, they wire together. So, the weights are updated like this: weight_change = learning_rate * input * output The information I've found on how this can be useful is pretty scarce, and I don't get it. In my current version of the simulator, the weights between

I am trying to get a feel for the difference between the various classes of machine-learning algorithms. I understand that the implementations of evolutionary algorithms are quite different from the implementations of neural networks. However, they both seem to be geared at determining a correlation between inputs and outputs from a potentially noisy set of training/historical data. From a qualitative perspective, are there problem domains that are better targets for neural networks as opposed to evolutionary algorithms? I've skimmed some articles that suggest using them in a complementary

The Context The context of this question is that I want to play around with Gene Expression Programming (GEP), a form of evolutionary algorithm, using Erlang . GEP makes use of a string based DSL called ' Karva notation '. Karva notation is easily translated into expression parse trees, but the translation algorithm assumes an implementation having mutable objects: incomplete sub-expressions are created early-on the translation process and their own sub-expressions are filled-in later-on with values that were not known at the time they were created. The purpose of Karva notation is that it