genetic-algorithm | 易学教程

Prevent inbreeding and monoculture in genetic algorithm (newbie question)

阅读更多关于 Prevent inbreeding and monoculture in genetic algorithm (newbie question)

问题 I am writing a genetic algorithm. My population quickly develops a monoculture. I am using a small population (32 individuals) with a small number of discrete genes (24 genes per individual) and a single point cross-over mating approach. Combine that with a roulette wheel selection strategy and it is easy to see how all the genetic diversity is lost in just a few dozen generations. What I would like to know is, what is the appropriate response? I do not have academic-level knowledge on GAs

What's differential evolution and how does it compare to a genetic algorithm?

阅读更多关于 What's differential evolution and how does it compare to a genetic algorithm?

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. 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 mostly the same-- both talk of chromosomes, both talk of genes, the genes are distinct alphabets, both talk of

When should I use genetic algorithms as opposed to neural networks? [closed]

阅读更多关于 When should I use genetic algorithms as opposed to neural networks? [closed]

Is there a rule of thumb (or set of examples) to determine when to use genetic algorithms as opposed to neural networks (and vice-versa) to solve a problem? I know there are cases in which you can have both methods mixed, but I am looking for a high-level comparison between the two methods. From wikipedia: A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. and: Neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to

Genetic Algorithms for computer security [closed]

阅读更多关于 Genetic Algorithms for computer security [closed]

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . I am in the process of choosing project for uni. And I am really interested on combining genetic algorithms and computer security. Therefore my question, Is it possible to use GA on any aspect for computer security? For example? . I was thinking something like a evolutionary

Ranking Selection in Genetic Algorithm code

阅读更多关于 Ranking Selection in Genetic Algorithm code

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 atom : typedef struct atom { int geno[VARS]; double pheno[VARS]; double fitness; } atom; Rank selection is

Fitness proportionate selection (roulette wheel selection) in Python

阅读更多关于 Fitness proportionate selection (roulette wheel selection) in Python

问题 I have a list of objects (Chromosome) which have an attribute fitness (chromosome.fitness is between 0 and 1) Given a list of such objects, how can I implement a function which returns a single chromosome whose chance of being selected is proportional to its fitness? That is, a chromosome with fitness 0.8 is twice as likely to be selected as one with fitness 0.4. I've found a few Python and pseudocode implementations, but they are too complex for this requirement: the function needs only a

GA written in Java

阅读更多关于 GA written in Java

问题 I am attempting to write a Genetic Algorithm based on techniques I had picked up from the book "AI Techniques for Game Programmers" that uses a binary encoding and fitness proportionate selection (also known as roulette wheel selection) on the genes of the population that are randomly generated within the program in a two-dimensional array. I recently came across a piece of pseudocode and have tried to implement it, but have come across some problems with the specifics of what I need to be

Which Java library/libraries for Genetic Algorithms? [closed]

阅读更多关于 Which Java library/libraries for Genetic Algorithms? [closed]

问题 I want to implement some simple genetic algorithms in Java . So far I found only JGAP . Did somebody has some experience with that? And do you know other Java libraries for GA? I do not want to write it my own as in GA written in Java and I have to use Java, so What is the most active genetic programming library? is also not that helpful. 回答1: I wrote the Watchmaker Framework so my opinions are not unbiased. ECJ and JGAP are the two most established options and probably the most comprehensive

Have you ever used a genetic algorithm in real-world applications?

阅读更多关于 Have you ever used a genetic algorithm in real-world applications?

I was wondering how common it is to find genetic algorithm approaches in commercial code. It always seemed to me that some kinds of schedulers could benefit from a GA engine, as a supplement to the main algorithm. Genetic Algorithms have been widely used commercially. Optimizing train routing was an early application. More recently fighter planes have used GAs to optimize wing designs. I have used GAs extensively at work to generate solutions to problems that have an extremely large search space. Many problems are unlikely to benefit from GAs. I disagree with Thomas that they are too hard to

Genetic algorithm resource [closed]

阅读更多关于 Genetic algorithm resource [closed]

Lately I'm interested in the topic of genetic algorithms, but I couldn't find any good resource. If you know any good resource, book or a site I would appreciate it. I have solid knowledge of algorithms and Artificial Intelligence but I'm looking for something with good introduction in Genetic Programming. Best references for me so far: Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg: a classic, still considered as the bible of GAs by many. An Introduction to Genetic Algorithms by Melanie Mitchell: more recent than the previous reference and packed with