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8 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, ...
Alvin Sartor's user avatar
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: ...
Thomas Wagenaar's user avatar
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 ...
NietzscheanAI's user avatar
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 ...
nbro's user avatar
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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 ...
Franck Dernoncourt's user avatar
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 ...
Tim Atkinson's user avatar
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 ...
nbro's user avatar
  • 40.9k
3 votes

How to avoid running out of solutions in genetic algorithm due to selection?

It is not true that the number of solutions necessarily decreases during the selection phase (if by solutions you mean the number of individuals in the population). The number of solutions is usually ...
nbro's user avatar
  • 40.9k
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 ...
NietzscheanAI's user avatar
2 votes
Accepted

Does fitness proportionate selection select multiple individuals?

Yes, you select the same individual multiple times, according to the distribution of fitness values. Typically, you have at least as many offspring as parents, so if you didn't replace parents in the ...
deong's user avatar
  • 611
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 ...
Oliver Mason's user avatar
  • 5,397
2 votes

How to avoid running out of solutions in genetic algorithm due to selection?

There are multiple ways to interpret those steps. The most common standard approaches are select two parents and produce two offspring; repeat until child population is the same size as parent ...
deong's user avatar
  • 611
2 votes

Can we use genetic algorithms to evolve datasets?

The paper Evolutionary Dataset Optimisation: learning algorithm quality through evolution (2019), by Henry Wilde et al., proposes a method to generate datasets with a genetic algorithm. Their goal is ...
nbro's user avatar
  • 40.9k
2 votes

Can we use genetic algorithms to evolve datasets?

This question raises a lot more questions. It seems like a solution looking for a problem, instead of the other way round. How do you measure the fitness of a feature? What would one of the "...
Robby Goetschalckx's user avatar
1 vote

how to apply crossover and mutation rates in genetic algorithm?

The remaining will be models selected from the original population based on the best performers (or some other user defined criteria). You don't do anything to those models - no crossover and no ...
Snehal Patel's user avatar
1 vote
Accepted

Is there some known pattern for selecting a batch of candidates for the next generation?

The most general descriptive frameworks covering what you are trying to do are: Sequential decision making (article is a stub, but the term a good launching point to discover different wys of ...
Neil Slater's user avatar
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1 vote

How to avoid running out of solutions in genetic algorithm due to selection?

This is a more complex question than it might initially seem. A genetic algorithm models a biological process,namely population genetics. No biological population evolves to a single cloned individual,...
Nick's user avatar
  • 251
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 ...
Nick's user avatar
  • 251
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 ...
S. McGrew's user avatar
  • 363

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