In a genetic algorithm there are a population of candidate solutions it’s called
1) Individuals
2) Creatures
3) Phenotypes to optimize a problem is evolved toward a best solutions. Each solution has a set of properties it is a chromosomes or a genotype, which they can be mutated, solutions are represented in binary as strings(0,1) also the other encodings are also possible.
The evolutions usually start from a population randomly generated individuals, and are an iterative process with populations in each iteration called a (generation). In each generation type have a fitness of every individual in population is evaluated the fitness is usually is the value of the function objective in the optimization problem being solved.…show more content… To be produced for each new solution, the pair of parents’ solutions is selected for breeding from all the pool selected previously.
In our DFP project we Use Select_fun to select two parent routes from the population for the purpose of crossover process. Child producing solution is using the above methods of the crossover and mutation; because new solutions are created typically shares in many of the characteristics of its "parents". New parent is selected for new child, and the process are continues until a new population of the solutions is appropriate size generated.
This process ultimately results in the next of generation population, chromosomes that are different from the initial generation. The average of the fitness will have increased by this procedure population, the best organisms from the first generation is selected for breeding, along with a small proportion of less fit solutions. These less-fit solutions ensure genetic diversity within genetic pool of the parents and so ensure the genetic diversity of the subsequent generation of the
