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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Is there any disadvantage of the maximum number of fitness function call as a stop criterion?

I'm studying different stop criteria in genetic algorithms and the advantages and disadvantages of each of them for evaluating different algorithms. One of these methods is the max number of fitness ...
85 views

How to decode P bits that represent a random weight generator?

So I've been tasked by my neural network professor at university to replicate the following research: Intelligent Breast Cancer Diagnosis Using Hybrid GA-ANN. Each chromosome represents a possible net,...
1 vote
152 views

Where or for what could genetic algorithms be used in the context of project management?

Where or for what could genetic algorithms (GA) be used in the context of project management (PM)? I thought about task dispatching, but I'm looking for other potential uses of GAs in the context of ...
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 ...
2k views

Scrabble game using machine learning

I've been thinking if machine learning can be used to play the game Scrabble. My knowledge is limited in the ML field, thus I've seeking some pointers :) I want to know how could I possibly build a ...
453 views

Is it easier to use back-propagation or genetic algorithms to teach an artificial intelligence?

I am making a very simple neural network for a school project, and I would like to know what the best and easiest way to "teach" a neural network would be. From what I know, backpropagation ...
1 vote
83 views

Is there any advantage of genetic algorithm (or programming) over Neural Networks? [closed]

I am planning to switch from neural networks to genetic algorithms (GA) and programming (GP). One of the main hassles of working with neural networks is that it requires a large amount of training ...
1k views

NEAT - Managing species across generations

I (mis?)understood the NEAT algorithm has the following steps: Create a genome pool with N random genomes Calculate each genome fitness Assign each genome to a species Calculate the adjusted fitness ...
1 vote
37 views

Applicability of Holland's Schema Theorem to Genetic Algorithms with Non-Binary Individual Representations

I'm currently working on a problem formulation that requires non-binary individual representations in a genetic algorithm (GA). I've been exploring Holland's Schema Theorem as a theoretical basis for ...
67 views

Which algorithm for production scheduling with multiple goals - alternative for genetic algorithm

I am currently using a genetic algorithm for optimising the production schedules of a factory that produces bespoke insulation panels. The factory has a list of bespoke panels that need to be produced ...
27 views

Less mutation rate is performing better in bigger neural network

I have a genetic AI neural network that evolves every generation and can add or remove neurons and change weights. It evolves good in first generations with mutation rate probability of e.g. ...
66 views

Best way to generate fitness landscape when using higher dimensional data

I'm using a GA to find the best set of parameters to maximize a fitness function. I want to draw a fitness landscape to visualize the effectiveness of the algorithm. The fitness function, calculated ...
28 views

What to do with not used variables when optimising a fitness function?

I'm optimising a fitness function using a GA. DNN is used to compute the fitness function using 4 input variables. The original data has 8 variables but to optimise DNN accuracy I have dropped 4 ...
540 views

How can I develop a genetic algorithm with a constraint on the sum of alleles?

I'm working on a genetic algorithm with a constraint on the sum of the alleles, e.g. if we use regular binary coding and a chromosome is 5-bits long I'd like to constrain it so that the sum of the ...
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Is it possible to learn to estimate the minimum value in a table?

Is it possible to classify or learn to estimate the minimum value in a table if the values are integer and represented 32 bits (and we can input all variables at the same moment, like in system on a ...
1 vote
73 views

What are most commons methods to measure improvement rate in a meta-heuristic?

When I run a meta-heuristics, like a Genetic Algorithm or a Simulated Annealing, I want to have a termination criterion that stops the algorithms when there is not any significant fitness improvement. ...
170 views

How should I train the players in the game of tag?

I have a simple game of tag, where red player tries to catch the blue player. Red player wins if it catches the blue player in under 10 seconds, but if not, then blue wins. My goal is to teach the ...
39 views

Which of these 3 mutation rates is the best in terms of performance?

I am need some comments since I am conducting experiments with 3 different mutation rates and hesitate to choose the best one. I ...
175 views

Crossover and Mutation function for value encoding [closed]

I have been trying to attempt writing a Genetic Algorithm using value encoding (fixed-length vectors of real numbers) instead of binary encoding. So far the code I have written works, but needs quite ...
36 views

agent based DNN with a loopback

I have a data problem with no direct reward mechanism,(test/train) good and fault solutions. Though over a long time period good decisions might be made. I've been searching for days now for an agent ...
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What is it meant by "cannot use gradients" in Genetic Algorithms?

While reading a book on introduction to GA, I stepped upon a chapter where some advantages and disadvantages of these algorithms were described. One of the mentioned disadvantages was "Cannot use ...
399 views

how to apply crossover and mutation rates in genetic algorithm?

I'm working with genetic programming and let's say I have the following operator: pop_size = 100 Crossover ratio = 0.4 Mutation Ratio = 0.2 Selection Ratio = 0.1 What is exactly the next generation ...
1 vote
43 views

Is there anything remotely as successful as backprop, but for training programs, not neural networks?

Backprop is used to train deep neural networks to remarkable success. Deep neural networks, on the other hands, can be seen as as a specific kind of computer function that receives inputs and produces ...
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 ...
1 vote
93 views

AIMA, Mutation in Genetic Algorithm

With regards to the highlighted line, the authors earlier stated that: The mutation rate, which determines how often offspring have random mutations to their representation. Once an offspring has ...
266 views

How to define a fitness function to make sure the best fitness value is 'close to 9' in genetic algorithm

I am learning about genetic algorithms (GA), but I encountered a question about the definition of the fitness function used in GA. I understand that the fitness function should return a scalar value (...
1 vote
103 views

What type of neural network has an unorganized structure?

I am looking for a network that has an unorganized structure like this, is feed-forward, does not have back-propagation functionality, and is trained with a genetic algorithm. What would I be looking ...
1 vote
33 views

Why we need to do mutation after crossover? [duplicate]

I am reading about genetic algorithms. In the genetic algorithm process we perform crossover and mutation. However, in the crossover, we already produce offspring, so then why do we also need to ...
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Is there a measure of AI relative strength, modified by resources?

For instance, Strength/Size$\times$Speed, where size and speed refer to memory and processing. We now have very strong, narrow AI, but they tend to run on fast hardware without volume restrictions. To ...
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Does pairing children with their parents cause any harm (in a genetic program)?

If you pair parents with their children (with a cross-over) does this prevent making individuals which are more fit or does this cause other side effects which are harmful to the genetic process? I ...
245 views

In novelty search, are the novel structures or behaviour of the neural network rewarded?

I have been reading a lot lately about some very promising work coming out of Uber's AI Labs using mutation algorithms enhanced with novelty search to evolve deep neural nets. See the paper Safe ...
785 views

When should I use Genetic Algorithms as opposed to Particle Swarm Optimization?

For which problems are Genetic Algorithms more suitable than Particle Swarm Optimization, and vice-versa? Are there any guidelines?
1 vote
248 views

Unable to achieve expected outputs using NEAT for the snake game

I am trying to implement NEAT for the snake game. My game logic is ready, which is working properly and NEAT configured. But even after 100 generations with 200 genomes per generation, the snakes ...
1 vote
384 views

How to calculate adjusted and normalized fitness when a higher raw fitness is better

I am reading Genetic Programming: On the Programming of Computers by Means of Natural Selection by John R. Koza. For calculating the "standardized fitness" of an individual, where a lower ...
1 vote
232 views

Why does the schema theorem of genetic algorithms hold?

I have been reading about the Schema Theorem - one of the first theorems from the field of evolutionary computing and genetic algorithms, largely responsible for justifying the use of genetic ...
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 ...
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 ...
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 ...
1 vote
77 views

Would it be a good idea to mutate half of the offspring of each GA generation 100% of the time and the other half 0% of the time?

I was reading about genetic algorithms, and to my understanding a genetic algorithm (GA) is an algorithm that starts with an initial population of chromosomes, where each chromosome has associated ...
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 ...
368 views

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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 ...
359 views

How to crossover chromosomes composed of genes that are tuples such that the elements of the tuples do not appear twice in the chromosome?

Each chromosome contains an array of genes, each gene contains a letter and a number, both letter and number can only exist once in each chromosome. ...
1 vote