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 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 ...
AlexanderB's user avatar
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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 ...
user366312's user avatar
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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 ...
CES's user avatar
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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 ...
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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. ...
Mahdyfo's user avatar
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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 ...
program1232123's user avatar
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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 ...
program1232123's user avatar
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1 answer
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Neat Python - Population size explodes because of high mutation rate [closed]

Someone has already asked a question about this. But I implemented the suggestion made in the comments without success. So I was wondering if anybody had a better idea. I changed the mutation power ...
eeqesri's user avatar
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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 ...
LearningLogic's user avatar
2 votes
2 answers
147 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 ...
MaLa's user avatar
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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 ...
No_Name's user avatar
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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 ...
Peter's user avatar
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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 ...
user3366592's user avatar
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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 ...
CTMA's user avatar
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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 ...
MaiaVictor's user avatar
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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 ...
learner's user avatar
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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 ...
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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 (...
DavidK's user avatar
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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 ...
coder's user avatar
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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 ...
Inverse Problem's user avatar
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1 answer
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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 ...
SandTh's user avatar
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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,...
JOSEPH CAROÈ's user avatar
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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 ...
Flux's user avatar
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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 ...
stats_noob's user avatar
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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 ...
Joseph Walker's user avatar
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271 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
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Is there a crossover that also considers that every index in the vector also influences the fitness function?

Is there a crossover that also considers that every index in the vector also influences the cost function? I have two vectors $v_1=[A_1, A_2, A_3, A_4, A_5]$ and $v_2=[A_5, A_3, A_2, A_1, A_4]$. The ...
MrPlanck's user avatar
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1 answer
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In the NEAT algorithm, what is the purpose of treating disjoint and excess genes differently?

In the NEAT algorithm, what is the purpose of treating disjoint and excess genes differently? They are treated so (or may be treated potentially) at least when calculating the distance between 2 ...
gerichhome's user avatar
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1 answer
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What exactly is the population in the problem of finding the best path in a network of nodes using genetic algorithms?

I have 17 nodes in my network with 3000 different paths in total. I have to select the path with highest available bandwidth, using genetic algorithm. I'm confused about the approach! Should I have ...
shadi's user avatar
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2 votes
0 answers
136 views

In NEAT, how do node numbers work?

I have read a lot of debates about node ids and such. I'm not 100% sure how it works, but I am assuming the next node added to a network would be the next number in that specific networks list? For ...
Steve Forbes's user avatar
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67 views

How does the paper implement NEAT without a global set tracking Innovations?

I have been reading this paper on NEAT and trying to implement the algorithm in C#. For the most part, I understand everything in the paper however, there are 2 things I don't understand that confuse ...
Steve Forbes's user avatar
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1 answer
326 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
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1 answer
374 views

Is a genetic algorithm efficient for a snake game?

I am working on a DIY project in which I want to be able to train a neural network to play Snake. Is a genetic algorithm an efficient way of training a network for this application? For a GA, what ...
XDead's user avatar
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3 votes
1 answer
372 views

What is the most computationally efficient genetic algorithm?

In researching genetic algorithms, it seems that there are various methods of selection and other operator methods that can significantly change the performance. For example, this picture contains ...
Ron Germano's user avatar
1 vote
1 answer
209 views

How to design fitness function for multiple objectives?

I am currently building a neural network with genetic algorithms that learns to fly a 2D drone to a target. My goal is that it achieves all tasks as fast as possible, but I want the drone to also fly ...
Rasmus's user avatar
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1 vote
1 answer
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How to handle equality constraints in the mutation operation of evolutionary algorithms?

I am new in evolutionary algorithms field. I have a chromosome of 6 variables (real variable), where the sum of these variables is equal to 1. I am looking for mutation formulas that can generate a ...
Rahim Brahimi's user avatar
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1 answer
357 views

Is it possible to optimize a multi-variable function with a reinforcement learning method?

I want to use RL instead of genetic or any other evolutionary algorithm in order to find the best parameter for a function. Here is the problem: Given a function $$f(x,y,z, \text{data}),$$ where $x$, $...
Arta Asadi's user avatar
1 vote
0 answers
84 views

How should the 1-point crossover and mutation be defined for the problem of finding the largest circle that does not enclose any point?

For a random scattering of points, in a bounded area, the goal is to find the largest circle that can be drawn inside those same bounds that does not enclose any points. Solving this problem with a ...
Rim Sleimi's user avatar
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0 answers
191 views

How can I select features for a symbolic regression problem to be solved with genetic programming?

I want to solve a symbolic regression problem with genetic programming. My dataset is similar to this one, but I have 30 features, and I want to use only the most sensitive features. I found this ...
Roman's user avatar
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1 vote
1 answer
577 views

How to deal with evolutionary/genetic fitness function that can have both negative and positive values?

I am optimising function that can have both positive and negative values in pretty much unknown ranges, might be -100, 30, 0.001, or 4000, or -0.4 and I wonder how I can transform these results so I ...
GKozinski's user avatar
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If one of the inputs to a neural network (that represents a policy) is noisy and degrades the performance, would this architecture solve the issue?

I'm using genetic algorithms to train deep reinforcement learning (DRL) agents, similarly to what was done in this paper. DRL policies are therefore represented by deep neural networks, which map ...
BGa's user avatar
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1 answer
286 views

How to design a fitness function for a problem where there are 2 objectives?

I am told to express a fitness function for a question I have been presented. I am unsure how I would express the function. In words, what I have written down makes sense but turning this into a ...
user avatar
3 votes
1 answer
236 views

Are Genetic Algorithms suitable for problems like the Knuth problem?

We all know that Genetic Algorithms can give an optimal or near-optimal solution. So, in some problems like NP-hard ones, with a trade-off between time and optimal solution the near-optimal solution ...
yaminoyuki's user avatar
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2 answers
178 views

How to detect that the fitness landscape of a genetic algorithm is changing over time?

I understand that in each generation of a genetic algorithm, that generation must re-prove it's fitness (and then the fittest of that population is taken for the next population). In this case, I ...
Slowat_Kela's user avatar
3 votes
2 answers
568 views

Are there any disadvantages to using a variable population size in genetic algorithms?

When implementing a genetic algorithm, I understand the basic idea is to have an initial population of a certain size. Then, we pick two individuals from a population, construct two new individuals (...
Slowat_Kela's user avatar
3 votes
1 answer
73 views

Is it possible that the fittest individuals in an Artificial Life population may be successful by not actively pursuing the rules of the environment?

I'm trying to understand Artificial Life (e.g. here for a simple background) in Computational Evolution. I understand that in this set of methods, you set up a dynamic environment (e.g. the ecology of ...
Slowat_Kela's user avatar
2 votes
1 answer
1k views

What is the impact of changing the crossover and mutation rates?

What is the impact of using a: low crossover rate high crossover rate low mutation rate high mutation rate
fathese's user avatar
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1 vote
1 answer
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Is there some known pattern for selecting a batch of candidates for the next generation?

I'm a beginner with a classic "racing car" sandbox and a homemade simple neural network. My pattern: Copy the "top car" (without mutation) to the next generation If there are ...
ker2x's user avatar
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1 vote
1 answer
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What is the difference between sensitivity analysis and parameter tuning?

I tried different values of genetic algorithm operators: many crossover rates from 20% to 80% many crossover rates from 1% to 20% varying the population size The study of different parameter values ...
fathese's user avatar
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0 answers
221 views

Genetic algorithm stuck and cannot find an optimal solution

I'm working on SLAP (storage location assignment problem) using genetic algorithm implemented manually in the C++ programming language. The problem is fairly simple, we do have ...
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