What is Crossover Probability & Mutation Probability in Genetic Algorithm or Genetic Programming?

Question

What is Crossover Probability & Mutation Probability in Genetic Algorithm or Genetic Programming ? Could someone explain them from implementation perspective!

Solution 1

Mutation probability (or ratio) is basically a measure of the likeness that random elements of your chromosome will be flipped into something else. For example if your chromosome is encoded as a binary string of lenght 100 if you have 1% mutation probability it means that 1 out of your 100 bits (on average) picked at random will be flipped.

Crossover basically simulates sexual genetic recombination (as in human reproduction) and there are a number of ways it is usually implemented in GAs. Sometimes crossover is applied with moderation in GAs (as it breaks symmetry, which is not always good, and you could also go blind) so we talk about crossover probability to indicate a ratio of how many couples will be picked for mating (they are usually picked by following selection criteria - but that's another story).

This is the short story - if you want the long one you'll have to make an effort and follow the link Amber posted. Or do some googling - which last time I checked was still a good option too :)

Solution 2

According to Goldberg (Genetic Algorithms in Search, Optimization and Machine Learning) the probability of crossover is the probability that crossover will occur at a particular mating; that is, not all matings must reproduce by crossover, but one could choose Pc=1.0.

Probability of Mutation is per JohnIdol.

Solution 3

It's shows the quantity of features which inherited from the parents in crossover!

Note: If crossover probability is 100%, then all offspring is made by crossover. If it is 0%, whole new generation is made from exact copies of chromosomes from old population (but this does not mean that the new generation is the same!).

Solution 4

Here might be a little good explanation on these two probabilities:

http://www.optiwater.com/optiga/ga.html

Johnldol's answer on mutation probability is exactly words that the website is saying:

"Each bit in each chromosome is checked for possible mutation by generating a random number between zero and one and if this number is less than or equal to the given mutation probability e.g. 0.001 then the bit value is changed."

For crossover probability, maybe it is the ratio of next generation population born by crossover operation. While the rest of population...maybe by previous selection or you can define it as best fit survivors

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Comments

[Amber]

en.wikipedia.org/wiki/Genetic_algorithm has links with explanations for both.

[Kiril]

Reddy, is this homework?

[Reddy]

no! I am implemening Genetic Algorithm in java for solving network partition problem. I am kinda confused what exactly crossover probability means? I could not find any article explaining cross over probability clearly. I trying to seek the help from one who already implemented it!