CODEWITHSUNDEEP
algorithmgenetic-algorithm
user2550882
user2550882

Reputation: 13

Genetic algorithm chromosome generation

I need to develop a system to select a team out of a database. Is it possible to use a Genetic algorithm to get the initial population (chromosomes) representing players as some identifier. Each identifier have its genes in a database which are used to apply various rules (such as requirements to be team leader, etc.).

Is GA helpful for such scenario?

Upvotes: 1

Views: 1272

Answers (1)

Soravux
Soravux

Reputation: 9983

Yes, it can be.

First, evolutionary algorithms work directly with the genotype of an individual. Stating that your are using identifiers to link an individual in the algorithm is either implementation details (useless for the question) and seems simply erroneous (you should load the genotype in memory for faster access).

Your problem is a simple combination problem. For a given number of players available n from which we want to form teams of size k, a total of n! / (k! ⋅ (n - k)!) combinations are possible. This is generally too much possibilities to handle on nowadays computing resources. Evolutionary algorithms allows (among others) the optimization of a given function too big for analytic resolution or where no analytic analysis exists.

You seem confused as to how to implement this kind of process. First, choosing a good data representation is important to get good results. You should first state every characteristics you want to optimize and what is their relation toward performance and if cross-relations affect global performance.

You should be careful, though: genetic algorithms can tend to get stuck in local maximums, be sure to keep your genetic diversity high by not punishing too hard relatively good solutions or with a steep selection phase.

That being said, the analysis I gave you was for a purely combinatorial view. From the point of view of a team, where context matters, evolutionary algorithms won't be efficient. For instance, if you need 3 attackers, 2 defenders and a goalkeeper, you should simply sort your player list three times, first according to the characteristics of a good attacker, then defender and finally goalkeeper and take the best elements (first elements after sorting) to compose your team. This will be way faster and give you an optimal result than using an evolutionary algorithm. Evolutionary algorithms such as genetic algorithsm would be of prime choice if you had no idea of the mechanics of the game played nor the inner workings of an optimal play.

Nevertheless, It is a good idea to begin toying with genetic algorithms to get a grasp of their possibilities and limitations. A good idea is to begin with a simple framework in a simple language such as deap or pyevolve in Python to try your ideas out.

Upvotes: 1

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