genetic-algorithm

I am trying to implement genetic algorithm for maximizing a function of n variables. However the problem is that the fitness values can be negative and I am not sure about how to handle negative values while doing selection. I read this article Linear fitness scaling in Genetic Algorithm produces negative fitness values but it's not clear to me how the negative fitness values were taken care of and how scaling factors a and b were calculated. Also, from the article I know that roulette wheel selection only works for positive fitness value. Is it the same for tournament selection as well ?

I have data set n x m where there are n observations and each observation consists of m values for m attributes. Each observation has also observed result assigned to it. m is big, too big for my task. I am trying to find a best and smallest subset of m attributes that still represents the whole dataset quite well, so that I could use only these attributes for teaching a neural network. I want to use genetic algorithm for this. The problem is the fittness function. It should tell how well the generated model (subset of attributes) still reflects the original data. And I don't know how to

I have a GA with a fitness function that can evaluate to negative or positive values. For the sake of this question let's assume the function u = 5 - (x^2 + y^2) where x in [-5.12 .. 5.12] y in [-5.12 .. 5.12] Now in the selection phase of GA I am using simple roulette wheel . Since to be able to use simple roulette wheel my fitness function must be positive for concrete cases in a population, I started looking for scaling solutions. The most natural seems to be linear fitness scaling . It should be pretty straightforward, for example look at this implementation . However, I am getting

I'm in the process of learning about simulated annealing algorithms and have a few questions on how I would modify an example algorithm to solve a 0-1 knapsack problem. I found this great code on CP: http://www.codeproject.com/KB/recipes/simulatedAnnealingTSP.aspx I'm pretty sure I understand how it all works now (except the whole Bolzman condition, as far as I'm concerned is black magic, though I understand about escaping local optimums and apparently this does exactly that). I'd like to re-design this to solve a 0-1 knapsack-"ish" problem. Basically I'm putting one of 5,000 objects in 10