8
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How do mutation and crossover work with real-valued chromosomes?
As @Thomas W said, you can be pretty immaginative when you're developing mutation and crossover methods. Each problem has its own caracteristics and, therefore, requires a different strategy.
BUT, ...
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7
votes
Accepted
How do mutation and crossover work with real-valued chromosomes?
You have a genome with certain genes:
genome = { GeneA: value, GeneB: value, GeneC: value }
So take for example:
...
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5
votes
Accepted
What is the difference between "mutation" and "crossover"?
The mutation is an operation that is applied to a single individual in the population. It can e.g. introduce some noise in the chromosome. For example, if the chromosomes are binary, a mutation may ...
- 41.4k
3
votes
What is the impact of changing the crossover and mutation rates?
The crossover rate, $p_c \in [0, 1]$, is a hyper-parameter that controls the rate at which solutions are subjected to crossover. So, the higher $p_c$, the more crossovers you perform, so the more ...
- 41.4k
3
votes
Accepted
Is elitism preferred over non-elitism in the cross-over operator?
First of all the answer to your question is largely dependent on the problem you are trying solve, the size of your population, the size of your problem's search space and the rest of your GA's hyper-...
- 850
3
votes
How does crossover work in a genetic algorithm?
As far as I know, there isn't a "specified correct way". The whole idea is that you want the population to converge and increase the sample rate in that more optimal looking place. What works best all ...
- 439
2
votes
Accepted
What is meant by "reproduction" in the description of this exercise?
The terminology of this exercise is not standard. What is referred to as "reproduction" in the exercise is usually referred to as "selection".
The term "reproduction" does indeed seem conceptually ...
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2
votes
Accepted
What does "In each generation, 25% of offspring resulted from mutation without crossover" mean in the context of NEAT?
In genetic algorithms, mutation without crossover simply means that part of the population is randomly changed. In this case this is applied to 25% of the population.
The remaining 75% either remain ...
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2
votes
Accepted
In NEAT, is it a good idea to give the same ID to node genes created from the same connection gene?
In NEAT, the innovation of a node does not affect the evolution directly. Only the connection genes and their innovation will matter. So you can simply have whole numbers as IDs under each Genome / ...
- 106
2
votes
Is there a crossover that also considers that every index in the vector also influences the fitness function?
Really you're entering the world in which you probably want to develop genetic operators that have meaning in your domain. You mention TSP, and correctly point out that the absolute position within ...
- 611
2
votes
Accepted
What is the most computationally efficient genetic algorithm?
First of all, for a lot of realistic problems, the fitness function evaluation is usually orders of magnitude greater in complexity than the rest of the genetic algorithm. This is not always true, but ...
- 411
1
vote
Does pairing children with their parents cause any harm (in a genetic program)?
Children pairing with their parents has a chance of harming the genetic process:
If a child pairs with one of it's parents it's possible to have identical fit individuals who pair with each other (and ...
- 131
1
vote
How do I determine the genomes to use for crossover in NEAT?
The original work on NEAT(Neuroevolution of augmenting topologies) was by Ken Stanley in 2002 at The University of Texas at Austin. The web page for the project is here I suggest you download and read ...
- 251
1
vote
Accepted
Do I have to crossover my node genes in NEAT, and how?
Okay, I'm first going to review how NEAT works. I hope this helps you model NEAT successfully as a whole, not just limited to your question.
We use neuro-evolution to create a specific behavior that ...
1
vote
Are there clever (fitness-based) crossover operators for binary chromosomes?
It's not obvious what you mean by "intelligent crossover".
However, it is common to use fitness-based selection of parents: individuals in the current population who have higher fitness are ...
- 363
1
vote
How can we design the mutation and crossover operations when the order of the genes in the chromosomes matters?
If I understood correctly, your problem is about finding the optimal way to execute a series of tasks in order to maximize the results, using Genetic Algorithms.
In few words, you're trying to ...
- 391
1
vote
How to handle infeasibility caused due to crossover and mutation in genetic algorithm for optimization?
You have two broad categories of options, prevention and repair.
Prevention means defining a crossover and mutation operator that try to be more intelligent about respecting the constraints. Suppose ...
- 611
1
vote
How to crossover chromosomes composed of genes that are tuples such that the elements of the tuples do not appear twice in the chromosome?
First, store the two parent chromosomes into a sorted dictionary (in terms of implementation, std::map in C++ might be a good option) where the key is the letter ...
- 953
1
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What is the difference between "mutation" and "crossover"?
I like to use the term, "recombination operator" rather than "crossover operator", because the latter term suggests a specific type of operation: constructing an offspring by switching corresponding ...
- 363
1
vote
Is there an efficient way to implement a random crossover of individuals stored in a matrix?
Firstly, before we commence I will recommend that you refer to a similar questions on the network https://stackoverflow.com/questions/828486/neural-net-optimize-w-genetic-algorithm
The majority of ML ...
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