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Questions tagged [genetic-algorithms]

For questions related to genetic algorithms (GAs), which are a form of evolutionary algorithms. A genetic algorithm is a method (more precisely, a metaheuristic) for solving optimization and search problems based on natural selection processes (that is, they use bio-inspired operators such as mutation, crossover, and selection).

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What exactly are genetic algorithms and what sort of problems are they good for?

I've noticed that a few questions on this site mention genetic algorithms and it made me realize that I don't really know much about those. I have heard the term before, but it's not something I've ...
Disenchanted Lurker's user avatar
18 votes
2 answers
6k views

How does novelty search work?

In this article, the author claims that guiding evolution by novelty alone (without explicit goals) can solve problems even better than using explicit goals. In other words, using a novelty measure as ...
rcpinto's user avatar
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14 votes
5 answers
9k views

Is a genetic algorithm an example of artificial intelligence?

Since human intelligence presumably is a function of a natural genetic algorithm in nature, is using a genetic algorithm in a computer an example of artificial intelligence? If not, how do they differ?...
WilliamKF's user avatar
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14 votes
2 answers
594 views

How should I encode the structure of a neural network into a genome?

For a deterministic problem space, I need to find a neural network with the optimal node and link structure. I want to use a genetic algorithm to simulate many neural networks to find the best network ...
Mithical's user avatar
  • 2,915
11 votes
2 answers
7k views

How do mutation and crossover work with real-valued chromosomes?

How exactly are "mutation" and "cross-over" applied in the context of a genetic algorithm based on real numbers (as opposed to just bits)? I think I understood how those two phases are applied in a "...
danidemi's user avatar
  • 213
10 votes
1 answer
3k views

What is the difference between reinforcement learning and evolutionary algorithms?

What is the difference between reinforcement learning (RL) and evolutionary algorithms (EA)? I am trying to understand the basics of RL, but I do not yet have practical experience with RL. I know ...
Single Malt's user avatar
10 votes
1 answer
3k views

What is the difference between memetic algorithms and genetic algorithms?

What is the difference between memetic algorithms and genetic algorithms? Is an individual's lifetime a learning part of memetic algorithms?
Nasser's user avatar
  • 211
9 votes
6 answers
1k views

Is artificial life really life or not?

I define Artificial Life as a "simulation" or "copy" of life. However, should it be considered a simulation or copy? If one had motivation and money, someone could theoretically ...
codeman's user avatar
  • 191
9 votes
6 answers
4k views

How to evaluate a NEAT neural network?

I'm trying to write my own implementation of NEAT and I'm stuck on the network evaluate function, which calculates the output of the network. NEAT as you may know contains a group of neural networks ...
Chris's user avatar
  • 193
9 votes
3 answers
502 views

Why is cross-over a part of genetic algorithms?

Genetic Algorithms has come to my attention recently when trying to correct/improve computer opponents for turn-based strategy computer games. I implemented a simple Genetic Algorithm that didn't use ...
Mithical's user avatar
  • 2,915
9 votes
1 answer
2k views

Should I use neural networks or genetic algorithms to solve Gomoku?

Currently, I'm doing a project that's about creating an AI to play the game Gomoku (it's like tic tac toe, but played on a 1515 board and requires 5 in a row to win). I have already successfully ...
Conway's user avatar
  • 93
9 votes
1 answer
151 views

Given a list of integers $\{c_1, \dots, c_N \}$, how do I find an integer $D$ that minimizes the sum of remainders $\sum_i c_i \text{ mod } D$?

I have a set of fixed integers $S = \{c_1, \dots, c_N \}$. I want to find a single integer $D$, greater than a certain threshold $T$, i.e. $D > T \geq 0$, that divides each $c_i$ and leaves ...
Ramzah Rehman's user avatar
8 votes
2 answers
333 views

Apart from Reinforcement Learning, are there any other machine learning approaches to play video games?

OpenAI's Universe utilizes RL algorithms. I also know that Q-learning has been used to solve some games. Are there any other ML approaches to solve games? For example, could we use genetic algorithms ...
U754V's user avatar
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8 votes
2 answers
9k views

How to create a good fitness function?

In genetic algorithms, a function called "fitness" (or "evaluation") function is used to determine the "fitness" of the chromosomes. Creating a good fitness function is one of the challenging tasks in ...
Abbas Ali's user avatar
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7 votes
2 answers
4k views

What is a Markov chain and how can it be used in creating artificial intelligence?

I believe a Markov chain is a sequence of events where each subsequent event depends probabilistically on the current event. What are examples of the application of a Markov chain and can it be used ...
WilliamKF's user avatar
  • 2,513
7 votes
1 answer
390 views

How to solve the problem of too big activations when using genetic algorithms to train neural networks?

I am trying to create a fixed-topology MLP from scratch (with C#), which can solve some simple problems, such as the XOR problem and MNIST classification. The network will be trained purely with ...
Joshua Jang's user avatar
7 votes
1 answer
2k views

Is elitism preferred over non-elitism in the cross-over operator?

There are two potential approaches when performing cross-over operation in genetic algorithms. Use only the elites in the pool, probably the ones that are also going to be directly transferred to the ...
Alireza's user avatar
  • 405
6 votes
1 answer
206 views

What evolutionary algorithms are there that model epigenetics?

What evolutionary algorithms are there that model or incorporate some notion of epigenetics? What are the pros/cons of those approaches? Are there vast insufficiencies or wide-open questions about ...
dynrepsys's user avatar
  • 1,363
6 votes
2 answers
1k views

Can neuroevolution be combined with gradient descent?

Is there any precedent for using a neuroevolution algorithm, like NEAT, as a way of getting to an initialization of weights for a network that can then be fine-tuned with gradient descent and back-...
benbyford's user avatar
  • 348
6 votes
1 answer
604 views

How does mating take place in NEAT?

In the Evolving Neural Networks through Augmenting Topologies (NEAT) paper it says (p. 110): The entire population is then replaced by the offspring of the remaining organisms in each species. ...
Miemels's user avatar
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6 votes
1 answer
718 views

How is the distance between pointers in Stochastic Universal Sampling determined?

I'm studying about different selection methods in genetic algorithms. My question is about the Stochastic Universal Sampling (SUS) selection method. I know that each individual will occupy a segment ...
helen's user avatar
  • 143
6 votes
1 answer
340 views

How to create and train (with mutation and selection) a neural network to predict the next state of a board?

I'm aiming to create a neural network that can learn to predict the next state of a board using the rules of Conway's Game of Life. Technically, I have three questions, but I felt that they needed to ...
Aric's user avatar
  • 275
5 votes
2 answers
1k views

What is a trap function in the context of a genetic algorithm?

What is a trap function in the context of a genetic algorithm? How is it related to the concepts of local and global optima?
mountaincloud's user avatar
5 votes
3 answers
167 views

Is it possible to separately evolve a part of the population?

In a classic example of a genetic algorithm, you would have a population and a certain amount of simulation time to evaluate it and breeding. Then proceed to the next generation. Is it possible, ...
mikerson's user avatar
5 votes
2 answers
1k views

Does NEAT require only connection genes to be marked with a global innovation number?

Does NEAT require only connection genes to be marked with a global innovation number? From the NEAT paper Whenever a new gene appears (through structural mutation), a global innovation number is ...
kuma's user avatar
  • 341
5 votes
2 answers
4k views

Is it possible to classify data using a genetic algorithm?

Is it possible to classify data using a genetic algorithm? For example, would it be possible to sort this database? Any example in Matlab?
Ricardo Pouças's user avatar
5 votes
2 answers
368 views

Do genetic algorithms also evolve?

After witnessing the rise of deep learning as automatic feature/pattern recognition over classic machine learning techniques, I had an insight that the more you automate at each level, the better the ...
Kayonga Arnauld's user avatar
5 votes
1 answer
3k views

Several questions regarding the NEAT algorithm [closed]

I've recently read the paper Evolving Neural Networks through Augmenting Topologies which introduces NEAT. I am now trying to prototype it myself in JavaScript. However, I stumbled across a few ...
Nigk's user avatar
  • 63
5 votes
1 answer
1k views

When do mutations in NEAT occur?

I read through the Evolving Neural Networks through Augmenting Topologies (NEAT) paper. I understand the algorithm now, but one thing is still unclear to me. When does the mutation occur and how ...
Miemels's user avatar
  • 389
5 votes
1 answer
583 views

What happens if 2 genes have the same connection but a different innovation number?

I have read the Evolving Neural Networks through Augmenting Topologies (NEAT) paper, but some doubts are still bugging me, so I have two questions. When do mutations occur? Between which nodes? When ...
Miemels's user avatar
  • 389
5 votes
1 answer
2k views

What are examples of optimization problems that can be solved using genetic algorithms?

I'm trying to learn how genetic algorithms can solve optimization problems. I have already learned how genetic algorithms can solve the knapsack, TSP and set cover problems. I'm looking for some other ...
Pablo's user avatar
  • 273
5 votes
1 answer
743 views

Is there an efficient way to implement a random crossover of individuals stored in a matrix?

I am using a GA to optimise an ANN in Matlab. This ANN is pretty basic (input, hidden, output) but the input size is quite large (10,000) and the output size is 2 since I have to classes of images to ...
user3952's user avatar
4 votes
3 answers
505 views

Could GA's determine fitness by "Fighting" against each other? [closed]

I am developing AI in the form of NEAT, and it has passed certain tasks like the XOR problem outlined in the NEAT Research Paper. In the XOR Problem, the fitness of a network was determined by an ...
Jaboyc's user avatar
  • 151
4 votes
2 answers
612 views

What's the difference between biological and artificial evolution?

I am trying to understand the difference between biological and artificial evolution. If we look at it in terms of genetics, in both of them, the selection operation is a key term. What's the ...
Germa Vinsmoke's user avatar
4 votes
2 answers
403 views

Do genetic algorithms "learn"?

I am currently working my way into Genetic Algorithms (GA). I think I have understood the basic principles. I wonder if the time a GA takes to go through the iterations to determine the fittest ...
user avatar
4 votes
1 answer
336 views

Does elitism cause premature convergence in genetic algorithms?

I have a genetic algorithm which is working fairly well. It's got all the standard operators, including initial random population, crossover ratio, mutation rate, degree of mutation, etc. This works ...
Pittsburgh DBA's user avatar
4 votes
2 answers
430 views

What is the difference between genetic algorithms and evolutionary game theory algorithms?

What is the difference between genetic algorithms and evolutionary game theory algorithms?
DRV's user avatar
  • 1,703
4 votes
1 answer
127 views

Can we use the Tierra approach to optimize machine code?

Thomas Ray's Tierra is a computer program which simulates life. In the linked paper, he argues how this simulation may have real-world applications, showing how his digital organisms (computer ...
olinarr's user avatar
  • 755
4 votes
2 answers
5k views

When should I use simulated annealing as opposed to a genetic algorithm?

What kind of problems is simulated annealing better suited for compared to genetic algorithms? From my experience, genetic algorithms seem to perform better than simulated annealing for most problems....
Abbas Ali's user avatar
  • 566
4 votes
1 answer
124 views

Can I compute the fitness of an agent based on a low number of runs of the game?

I'm developing an AI to play a card game with a genetic algorithm. Initially, I will evaluate it against a player that plays randomly, so there will naturally be a lot of variance in the results. I ...
OrangeMan's user avatar
  • 237
4 votes
3 answers
813 views

How to implement a neural network for Flappy Bird in Python? [closed]

I am new in the field of AI. I am working to create the flappy bird using Genetic Algorithm. After reading and seeing some examples, I saw that most implementations use a Neural Network + Genetic ...
souleatzz's user avatar
4 votes
2 answers
287 views

Are Genetic Algorithms suitable for a problem with a non-unique optimal solution?

I was wondering if a genetic algorithm is useful if the optimization problem has several optimal solutions. My thought was that I should not use it since when combining two members of a population who ...
nuemlouno's user avatar
  • 141
4 votes
1 answer
73 views

Are there any strategies that would help me visualize the 'behavior space' and make a novelty function?

In “Abandoning Objectives: Evolution through the Search for Novelty Alone”, it is explained how the novelty search is a function that is domain specific, depending on the differing behaviors that can ...
DoubleDouble's user avatar
4 votes
1 answer
195 views

Using NEAT, will the child of two parent genomes always have the same structure as the more fit parent?

I'm trying to implement the NEAT Algorithm using c#, based off of Kenneth O. Stanley's paper. On page 109 (12 in the pdf) it states "Matching genes are inherited randomly, whereas disjoint genes (...
Alex Charters's user avatar
4 votes
1 answer
517 views

How do I use a genetic algorithm to generate the scores of an evaluation function for alpha-beta pruning?

I have created a Gomoku (5 in a row) AI using Alpha-Beta Pruning. It makes moves on a not-so-stupid level. First, let me vaguely describe the evaluation function of the Alpha-Beta algorithm. When it ...
Dashadower's user avatar
4 votes
1 answer
1k views

What is an appropriate fitness function for a simulated self-driving car?

I have been working for ages on a neuro-evolution AI program, where cars learn how to race around a track. Presently, I have a rudimentary fitness function that awards points for every degree ...
Aniekan Umoren's user avatar
4 votes
0 answers
773 views

How to perform classification with NEAT-Python?

I am trying to do classification using NEAT-python for the first time, and I am having difficulty getting the accuracy rate. I tried the same problem with an ANN and was able to get a good accuracy ...
Linkuz's user avatar
  • 41
4 votes
1 answer
810 views

Traveling salesman problem variant: which algorithm to choose?

I have an industrial problem which I'm trying to cast as a Traveling Salesman problem (TSP) in 3D euclidian space. There are physical limitations which implies that some subpaths may or may not be ...
Oliver's user avatar
  • 41
3 votes
2 answers
195 views

Why do we apply the mutation operation after generating the offspring?

Why do we apply the mutation operation after generating the offspring, in genetic algorithms?
Nimra Malik's user avatar
3 votes
2 answers
479 views

Is it possible to perform neuroevolution without a fitness function?

My question is about neuroevolution (genetic algorithm + neural network): I want to create artificial life by evolving agents. But instead of relying on a fitness function, I would like to have the ...
LU15.W1R7H's user avatar