fitness function and selection methods

Evaluation and Fitness Function :

The objective function is used to provide a measure of how individuals have performed in the problem domain. In the case of a minimization problem, the most fit individuals will have the lowest numerical value of the associated objective function. The evaluation function of a GA is used to determine the fitness of an individual.


Selection:
Selection operation is the main operation in genetic algorithm. It is used to choose the best fit individuals in the population to create the new individuals. New individuals will participate in further genetic operations to create the next generation of population. The next generation of population is created with a hope to reach the optimal solution. Many selection methods have been proposed. The most common types are :
1. Roulette wheel selection.
2. Rank selection.
3. Tournament selection.
4. Elitism.


1- Roulette Wheel Selection :
Roulette wheel selection is most common selection method used in genetic algorithms for selecting useful individuals (solutions) for crossover and mutation. In roulette wheel selection, as in all selection methods, possible solutions are assigned a fitness by the fitness function. This fitness level is used to associate a probability of selection with each individual.


2- Tournament Selection:
This method randomly chooses a set of individuals and picks out the best individual for reproduction. The number of individuals in the set is called the tournament size.


3- Elitist Selection
In elitism, an approach to selection, best individual is carried forward to next generation as such. Simple elitist strategy enforces the reservation of best solution found so far unless a new best individual is discovered.
Elitism helps to speed up the search process, but its drawback is contribution to decrease in diversity of the population. Good balance has to be maintained between exploitation and exploration while implementing elitism.


4- Rank Selection:
In ranking selection, the individuals in the population are sorted from best to worst according to their fitness values. Each individual in the population is assigned a numerical rank based on fitness, and selection is based on this ranking rather than differences in fitness.