fitness and selection method in genetic algorithm

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.
While candidate solutions with a higher fitness will be less likely to be eliminated, poor solution may has a chance to survive in the selection process; this is an advantage, it may include some component which could prove useful following the recombination process.