Genetic Algorithms Population - Genetic Algorithms

What is Genetic Algorithms Population?

Population is a subset of answers in the present generation. It can also be distinct as a set of DNAs. There are several things to be kept in mind when dealing with GA population −

  • The variety of the population should be upheld then it might lead to premature convergence.
  • The population size should not be kept very large as it can cause a GA to slow down, while a smaller population might not be enough for a good mating pool. Therefore, an optimal population size needs to be decided by trial and error.

The population is typically defined as a two dimensional array of – size population, size x, chromosome size.

Population Initialization

There are two primary methods to make ready a population in a GA. They are −

  • Random Initialization − Populate the initial population with totally random solutions.
  • Heuristic initialization − Populate the initial population using a known heuristic for the problem.

It has been detected that the entire population must not be initialized using a heuristic, as it can effect in the population having alike solutions and very little variety. It has been experimentally detected that the random solutions are the ones to drive the population to optimality. So, with heuristic initialization, we just seed the population with a couple of good solutions, filling up the rest with random solutions rather than filling the entire population with heuristic based solutions.

It has also been detected that heuristic initialization in some cases, merely effects the initial fitness of the population, but in the end, it is the diversity of the solutions which lead to optimality.

Population Models

There are two population models widely in use −

Steady State

In steady state GA, we make one or two off-springs in each iteration and they substitute one or two individuals from the population. A steady state GA is also known as Incremental GA.

Generational

In a generational model, we generate ‘n’ off-springs, where n is the population size, and the entire population is replaced by the new one at the end of the iteration.

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