Research 1 - Genetic Algorithm
Genetic algorithm is an adaptive methods used to find optimum solutions for problems. It is an evolutionary algorithm. Genetic Algorithm is a stochastic search method. The main principle behind GA is survival of fittest. The evolutions over many generations should result in surviving only the fittest features included in the chromosomes.
It is a powerful general purpose optimization tool which model the principal of evolution. It can be viewed as a search heuristic which represents the natural evolution. Genetic algorithms are mostly used to solve problems which are impossible to solve using formulas.
Genetic algorithm is a population based search. Genetic algorithm uses evolution theorem to learn. Evolution is the process of change in all forms of life over generations, and evolutionary biology is the study of how evolution occurs. It contains genes and chromosomes. Genetic algorithm operates on a population of coded solutions which are selected according to the quality and they are the basis of new generation of solutions.
Genetic Algorithm
{
Generate initial population Pt
Evaluate population Pt
While stopping criteria not satisfied Repeat
{
Select some elements from Pt to copy into Pt+l
Crossover some elements of Pt and put into Pt+l
Mutate some elements of Pt and put into Pt+l
Evaluate new population Pt+l
Pt = Pt+l
}
}
Chromosome Representation
Resource Constraint Scheduling
Genetic Algorithm