genetic-algorithm

I am having trouble creating a Genetic Algorithm in java. I am competing in an online GA contest. I am trying to save the best result each time back into index 0, but it just becomes a reference to the original index. Meaning when I evolve the rest of the indexes, if it evolves the best members original index I lose it. I have tried shimming it with a getClone method that converts the objects data to and int array and creates a new object from it. Individual class: class Individual { public int[] angle; public int[] thrust; public double fitness; public Individual(){ angle = new int[2]; thrust

E.g. I have two random representatives 1 6 8 9 0 3 4 7 5 and 3 6 5 7 8 5 What are the ways to crossover them? Add some empty numbers (or operations or sth) on the end of every genotype so they will have the same size? 3 6 5 7 8 5 -1 -1 -1 where -1 means nothing? Or copy few number from first genotype and some from second? What is the way you use? If you already have variable length chromosomes, then it shouldnt matter how you do it, you just need to select a crossover point for each of them, and then crossover as normal. For example using your chromosomes, I have selected two points (.) at

I'm performing an operation, lets call it CalculateSomeData. CalculateSomeData operates in successive "generations", numbered 1..x. The number of generations in the entire run is fixed by the input parameters to CalculateSomeData and is known a priori. A single generation takes anywhere from 30 minutes to 2 hours to complete. Some of that variability is due to the input parameters and that cannot be controlled. However, a portion of that variability is due to things like hardware capacities, CPU load from other processes, network bandwidth load, etc. One parameter that can be controlled per

I have X number of students, where X is a multiple of 6. I now want to split up the students into groups of 6. I have a function that measures how "good" a group of 6 is (lets say it's a black box that runs in constant time for now). By splitting up the students, and then calling my function on each group to measure it's goodness, and then summing up the goodness of each group, I'm able to measure how "good" a certain set of groups is. I'm trying to create an algorithm that will group the students in a way so that the total goodness of all the groups is maximized, and no group has an

I'm considering a hypothetical problem, and looking for guidance on how to approach solving the problem, from an algorithmic point of view. The Problem: Consider a university. You have the following objects: Teaching staff. Each staff member teaches one or more papers. Students. Each student takes one or more papers. Rooms. Rooms hold a certain number of students, and contain certain types of equipment. Papers. Require a certain type of equipment, and a certain amount of time each week. Given information about enrollments (i.e.- how many students are enrolled in each paper, and what staff are

I've been working on an algorithm, where I need to choose n individuals from a population of size k, where k is much bigger than n. All individuals have a fitness value, therefore the selection should favor higher fitness values. However, I don't want to simply choose best n individuals, the worse ones should have a chance also. (Natural selection) So, I decided to find the min and max fitness values within population. So, any individual would have p = (current - min) / (max - min) probability to be chosen, but I can not just iterate over all of them, roll the dice and choose one if the