evolutionary-algorithm

Genetic algorithms: How to do crossover in “subset” problems?

由 耗尽温柔 提交于 2019-12-12 08:49:34
问题 I have a problem which I am trying to solve with genetic algorithms. The problem is selecting some subset (say 4) of 100 integers (these integers are just ids that represent something else). Order does not matter, the solution to the problem is a SET of integers not an ordered list. I have a good fitness function but am having trouble with the crossover function. I want to be able to mate the following two chromosomes: [1 2 3 4] and [3 4 5 6] into something useful. Clearly I cannot use the

All versions of differential evolution algorithm [closed]

由 别来无恙 提交于 2019-12-12 02:55:27
问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 5 years ago . explain all updates in the basic algorithm of differential evolution. i am not able to find all versions of this algorithm. explain all versions of this algorithm as a survey and i am not clearly understand the theory behind this algorithm as given in the Wikipedia. Wikipedia also define only basic

What effect do crossover probabilities have in Genetic Algorithms/Genetic Programming?

由 本小妞迷上赌 提交于 2019-12-11 08:23:42
问题 Can any one give an example of crossover probability? I would like to know what is the benefits of determining crossover probability, and also what effect it has in genetic algorithms or genetic programming. 回答1: Crossover probability doesn't have a benefit by definition. It is merely a parameter that allows you to adjust the behavior of a genetic algorithm. Lowering the crossover probability will let more individuals continue in the next generation unchanged. This may or may not have a

C# code or algorithm to quickly calculate distance between large strings? [closed]

由 十年热恋 提交于 2019-12-10 17:44:34
问题 It's difficult to tell what is being asked here. This question is ambiguous, vague, incomplete, overly broad, or rhetorical and cannot be reasonably answered in its current form. For help clarifying this question so that it can be reopened, visit the help center. Closed 7 years ago . Hi and thanks for looking! Background I have an XML file that contains 1900 nodes which themselves contain a string of encoded data of about 3400 characters. As part of a use case for an application I am

Pareto Optimal Front

由 旧街凉风 提交于 2019-12-10 00:01:12
问题 I am trying to obtain the pareto optimal front for the two fitness functions. I sorted the undominated solutions by using a dummy matrix that allocated "ones" in the matrix for any undominated solution. When I plot the pareto front it keeps including points that I know are not part of the pareto optimal. However, I cannot seem to find the cause of this problem. Any help would be really appreciated. for i = 1:1000 f1(i) = x(i,1)^2; f2(i) = (x(i,1)-2)^2; end store = zeros(1000,1); for i = 1

How to utilize Hebbian learning?

由 与世无争的帅哥 提交于 2019-12-09 04:08:26
问题 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

Algorithm to optimize # threads used in a calculation

由 别来无恙 提交于 2019-12-05 16:14:00
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

NSGA-II ( Non- Dominating Sorting Algorithm )

由 橙三吉。 提交于 2019-12-05 05:27:09
问题 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. 回答1: You can find some real-life applications by just searching : "NSGA-II + applications" in Google Scholar : http://scholar.google.com/scholar?start

Evolutionary Algorithms: Optimal Repopulation Breakdowns

由 只谈情不闲聊 提交于 2019-12-04 11:56:24
问题 It's really all in the title, but here's a breakdown for anyone who is interested in Evolutionary Algorithms: In an EA, the basic premise is that you randomly generate a certain number of organisms (which are really just sets of parameters), run them against a problem, and then let the top performers survive. You then repopulate with a combination of crossbreeds of the survivors, mutations of the survivors, and also a certain number of new random organisms. Do that several thousand times, and

Genetic algorithms: How to do crossover in “subset” problems?

由 不羁岁月 提交于 2019-12-04 06:14:55
I have a problem which I am trying to solve with genetic algorithms. The problem is selecting some subset (say 4) of 100 integers (these integers are just ids that represent something else). Order does not matter, the solution to the problem is a SET of integers not an ordered list. I have a good fitness function but am having trouble with the crossover function. I want to be able to mate the following two chromosomes: [1 2 3 4] and [3 4 5 6] into something useful. Clearly I cannot use the typical crossover function because I could end up with duplicates in my children which would represent
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