All Questions
Tagged with genetic-algorithms evolutionary-algorithms
56 questions
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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 ...
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2
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544
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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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84
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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 ...
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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 ...
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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 ...
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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 ...
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1
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702
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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 ...
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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 ...
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650
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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 ...
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1
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237
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What are trap functions in genetic algorithms? [duplicate]
What are trap functions in genetic algorithms? Suppose you ran a GA with a trap function and examined the population midway through the run. Can someone explain what you would expect the population to ...
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2
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What is meant by gene, chromosome, population in genetic algorithm in terms of feature selection?
I am trying to understand the genetic algorithm in terms of feature selection and these features are extracted using a machine learning algorithm.
Let's suppose I have data of heart rate for 3 minutes ...
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Crossover method for gene value containing a set of values
I have a chromosome where each gene contain s set of values. Like the following:
chromosome = [[A,B,C],[C,B,A],[C,D,],[],[E,F]]
The order in each gene values matters. (A,B,C is different to A,C,B)
...
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1
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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 ...
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How to effectively crossover mathematical curves?
I'm trying to optimize some reflective properties of curves of the form:
$a_1x^n+a_2x^{n-1}+a_3x^{n-2} + ... + a_n + b_1y^n+b_2y^{n-1}+b_3y^{n-2} + ... + b_n = 0$
which is basically the curve that ...
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What are evolutionary algorithms for topology and weights evolving of ANN (TWEANN) other than NEAT?
I wonder, if there are other than NEAT approaches to evolving architectures and weights of artificial neural networks?
To be more specific: I am looking for projects/frameworks/libraries that use ...
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2
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216
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Are there clever (fitness-based) crossover operators for binary chromosomes?
While studying genetic algorithms, I've come across different crossover operations used for binary chromosomes, such as the 1-point crossover, the uniform crossover, etc. These methods usually don't ...
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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 ...
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462
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What is the difference between genetic algorithms and evolutionary game theory algorithms?
What is the difference between genetic algorithms and evolutionary game theory algorithms?
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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 ...
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How to calculate multiobjective optimization cost for ordinary problems?
What I did:
Created a population of 2D legged robots in a simulated environment. Found the best motor rotation values to make the robots move rightward, using an objective function with Differential ...
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How can I solve the linkage problem in genetic algorithms?
In a genetic algorithm, the order of the genes on a chromosome can have a significant effect on the performance (capacity to generate adaptation) of the genetic algorithm, where two or more genes ...
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109
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Can we automate the choice of the hyper-parameters of the evolutionary algorithms?
Certain hyper-parameters (e.g. the size of the offspring generation or the definition of the fitness function) and the design (e.g. how the mutation is performed) of evolutionary algorithms usually ...
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751
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Exploding population size in neat-python
I am trying to make my AI win the board game "Catan" against my friends.
Therefore i am using the python implementation of NEAT.
As I changed the values of ...
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What qualifies as 'fitness' for a genetic algorithm that minimizes an error function?
Suppose I have a set of data that I want to apply a segmented regression to, fitting linearly across the breakpoint. I aim to find the offsets and slopes of either line and the position of the ...
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338
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Can we evolve 0 and 1? [closed]
Is it possible to combine or create conditional statements of 0 and 1, and optimize with an evolutionary algorithm (given that all computers use a binary system)?
There may be an algorithm that maps ...
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Shortest route GA: One loop through one dataset vs multiple loops through subsets of the same data?
I've a rather simple question for a school project. We're developing a GA solution for the following problem:
Chromosome: A location with lat-lon coords. There are two types of locations - up to 15 ...
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1
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Is there a reason evolutionary algorithms are language-bound in research material?
I've been working on genetic algorithms & evolutionary strategies for a while now in a research context. Across the vast majority of the articles and content I've read, every single one of them ...
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2
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203
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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?
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10k
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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 ...
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308
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How can a genetic algorithm adapt and get better in a changing environment?
I've just started studying genetic algorithms and I'm not able to understand why a genetic algorithm can improve if, at each learning, the 'world' that the population encounters change. For example, ...
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What are the available selection methods in genetic algorithms?
In a genetic algorithm, there are different steps. One of those steps is the selection of chromosomes for reproduction. What are the available selection strategies in genetic algorithms?
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NEAT + Keras : reproducibility problem (World Models implementation)
I'm trying to apply the World Models architecture to the Sonic game (using the gym-retro library).
My problem concerns the evolutionnary algorithm part that I use as the controller (worldmodels = ...
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How do I optimize a specific function using a genetic algorithm?
I recently learned about genetic algorithms and I solved the 8 queens problem using a genetic algorithm, but I don't know how to optimize any functions using a genetic algorithm.
$$
\begin{array}{r}
\...
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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 ...
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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 ...
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2
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642
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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 ...
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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 ...
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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 ...
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1
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1k
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Does fitness proportionate selection select multiple individuals?
Does fitness proportionate selection select multiple individuals?
So, I read on Wikipedia and on multiple Stack Exchange threads about fitness proportionate selection or rather roulette selection, but ...
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1
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2k
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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 ...
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1
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273
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Have evolutionary algorithms been used for engineering design?
Recently, I've been looking recently into what uses AI - specifically evolutionary algorithms - may have in automating engineering design. For a long time, there have been algorithms that solve ...
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2
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372
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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 ...
3
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1
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717
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How can one use Genetic Algorithms to solve the "15 Puzzle" (Mystic Square)?
How would one go about solving the 15 squares puzzle using a Genetic Algorithms approach? In particular, I'd like to understand how you would
represent the "chromosome" in the evolving system. That ...
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How to represent the weights of a neural network as binary strings for a genetic algorithm?
I want to train my neural network by evolution, that is I want to recombine the weights of the best performing neural networks in each evolution cycle or generation. My initial instinct was 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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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?
6
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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.
...
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588
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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 ...
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1
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1k
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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 ...
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2
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2k
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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?
