genetic-algorithm

问题 I was wondering if anyone knew any intuitive crossover and mutation operators for paths within a graph? Thanks! 回答1: Question is a bit old, but the problem doesn't seem to be outdated or solved, so I think my research still might be helpful for someone. As far as mutation and crossover is quite trivial in the TSP problem, where every mutation is valid (that is because chromosome represents an order of visiting fixed nodes - swapping order then always can create a valid result), in case of

问题 How do you find an optimum learning rule for a given problem, say a multiple category classification? I was thinking of using Genetic Algorithms, but I know there are issues surrounding performance. I am looking for real world examples where you have not used the textbook learning rules, and how you found those learning rules. 回答1: Nice question BTW . classification algorithms can be classified using many Characteristics like: What does the algorithm strongly prefer (or what type of data that

问题 I try to learn and implement a simple genetic algorithm library for my project. At this time, evolution, selection of population is ready, and I'm trying to implement a simple good mutation operator like the Gaussian mutation operator (GMO) for my genetic evolution engine in Java and Scala. I find some information on Gaussian mutation operator (GMO) into the paper A mutation operator based on a Pareto ranking for multi-objective evolutionary algorithms (P.M. Mateo, I. Alberto), page 6 and 7.

问题 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

问题 I need to solve simultaneous linear equations (5 equations with 7 unknowns i.e an under-determined problem) where the variables vary over a wide range of [0 - 1,00,000]. Can someone suggest what fitness function I should use? 回答1: I guess you are referring to a system of 5 linear equations with 7 variables. This paper seems to show what you're looking for. You basically need to define a cost function and use the GA to minimize it. Search the pdf for "fitness function" to see exactly how to do

问题 I came across this interesting video on YouTube on genetic algorithms. As you can see in the video, the bots learn to fight. Now, I have been studying neural networks for a while and I wanted to start learning genetic algorithms.. This somehow combines both. How do you combine genetic algorithms and neural networks to do this? And also how does one know the error in this case which you use to back-propagate and update your weights and train the net? And also how do you think the program in

问题 This is in response to an answer given in this question: How to create a good evaluation function for a game?, particularly by @David (it is the first answer). Background : I am using a genetic algorithm to optimize the hyper parameters in a game playing agent that is using minimax / alpha beta pruning (with iterative deepening). In particular, I would like to optimize the heuristic (evaluation) function parameters using a genetic algorithm. The evaluation function I use is: f(w) = w * num_my

问题 My question is linked with this one: Roulette-wheel selection in Genetic algorithm. Population needs to be sorted first? If we don't sort the population what is the way of organizing roulette wheel selection for it? Surely, we have to search in linear way now. Have you got any code snippets in C++ or Java for this case? 回答1: The population does not need to be sorted at all - the key to roulette selection is that the probability of a given individual being selected for reproduction is

问题 From what I've read so far they seem very similar. Differential evolution uses floating point numbers instead, and the solutions are called vectors? I'm not quite sure what that means. If someone could provide an overview with a little bit about the advantages and disadvantages of both. 回答1: Well, both genetic algorithms and differential evolution are examples of evolutionary computation. Genetic algorithms keep pretty closely to the metaphor of genetic reproduction. Even the language is

问题 I need code for the ranking selection method on a genetic algorithm. I have create roulette and tournament selections method but now I need ranking and I am stuck. My roulette code is here (I am using atom struct for genetic atoms) : const int roulette (const atom *f) { int i; double sum, sumrnd; sum = 0; for (i = 0; i < N; i++) sum += f[i].fitness + OFFSET; sumrnd = rnd () * sum; sum = 0; for (i = 0; i < N; i++) { sum += f[i].fitness + OFFSET; if (sum > sumrnd) break; } return i; } Where