Knowledge Engineering

Genetic Algorithms iteratively generate new solutions from current set of solutions and replace some or all of the existing population with the newly created members. The iteration of the Genetic Algorithms is explained below:

• An initial population of strings is created randomly.



• Measure the goodness/ strength of each individual in the population.



• Select individuals in the parent pool for the creation of next generation.



• New individuals (Offsprings) are created by performing crossover and/ or mutation on the selected individuals.



• The new population is tested to see if it satisfies the stopping criteria. If it satisfies, stop the loop; otherwise the next genetic algorithms iteration is performed.