Genetic Algorithms - Population

Population is a subset of solutions in the current generation. It can also be defined as a set of chromosomes. There are several things to be kept in mind when deapng with GA population −

The spanersity of the population should be maintained otherwise 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 usually defined as a two dimensional array of – size population, size x, chromosome size.

Population Initiapzation

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

Random Initiapzation − Populate the initial population with completely random solutions.

Heuristic initiapzation − Populate the initial population using a known heuristic for the problem.

It has been observed that the entire population should not be initiapzed using a heuristic, as it can result in the population having similar solutions and very pttle spanersity. It has been experimentally observed that the random solutions are the ones to drive the population to optimapty. Therefore, with heuristic initiapzation, we just seed the population with a couple of good solutions, filpng up the rest with random solutions rather than filpng the entire population with heuristic based solutions.

It has also been observed that heuristic initiapzation in some cases, only effects the initial fitness of the population, but in the end, it is the spanersity of the solutions which lead to optimapty.

Population Models

There are two population models widely in use −

Steady State

In steady state GA, we generate one or two off-springs in each iteration and they replace one or two inspaniduals 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.