7
votes
Accepted
Why is cross-over a part of genetic algorithms?
Mutation is usually defined to be a global operator, i.e. iterated mutation is (eventually) capable of reaching every point in the vector space defined by the geneome. In that sense, mutation alone is ...
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7
votes
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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6
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
Why is cross-over a part of genetic algorithms?
Crossover allows to combine two parents (vs. mutation, which only uses one parent). In some cases, it is useful (e.g., if you train a FPS bot, if one parent is good at shooting and another parent is ...
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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 ...
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4
votes
Why is cross-over a part of genetic algorithms?
When thinking about crossover its important to think about the fitness landscape.
Consider a hypothetical scenario where we are applying a genetic algorithm to find a solution that performs well at ...
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4
votes
Why do we apply the mutation operation after generating the offspring?
The mutation operation is (usually) needed to introduce new genes not found in the population.
For example, suppose that you have 4 possible genes $A$, $B$, $C$, and $D$, and that your chromosomes ...
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3
votes
Accepted
How to find optimal mutation probability and crossover probability?
As @Oliver Mason says, picking the parameters that control the behavior of a GA (which are sometimes called "hyperparameters") is historically more of an art than a science.
The evolutionary ...
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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 ...
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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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1
vote
How to handle equality constraints in the mutation operation of evolutionary algorithms?
If X is your 6D vector and m(X) is the mutated version of X, then you can renormalise the mutant back to unity by dividing by the sum of X, i.e. X' = m(X)/sum(X).
However, I encourage you figure out ...
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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 ...
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1
vote
Why do we apply the mutation operation after generating the offspring?
Mutation is used to maintain diversity in the solutions. Crossover alone cannot do this.
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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
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 ...
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1
vote
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 ...
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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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