Citation Context

...e, two and several parts of parents’ chromosomes are exchanged in one, two and uniform crossover respectively. In Mutation, some parts of chromosomes randomly change in order to have better performance and escape from local optima. It is possible to lose the best chromosomes when new chromosomes are created by crossover and mutation operators. Elitism is a method which copies the best chromosome (or a few best chromosomes) to new population and the rest is created by the mentioned methods. Therefore, this method increases the performance of GA, because it avoids losing the best-found solution [48]. The above steps have been illustrated in Fig. 5. Although GA has been extensively used in various problems, it suffers from some disadvantages [49, 50, 51] such as: 1. Using complex operators for selection and cross over. 2. Unpredicted results. 3. Premature convergence rate. 4. Trapping into local optima. 5. Taking long run-time. 6. Weak local search. Z. Beheshti et al. 12 7. .Difficult encoding scheme Fig. 4 GA crossover Step 1: Start. Step 2: Create first generation of chromosomes. Step 3: Define Parameters and fitness function. Step 4: Calculate the fitness of each individual chromosome....

Citation Context

...) is usually randomly initialized. The individuals in the population then pass through a procedure of simulated "evolution" by means of randomized processes of selection, crossover, and mutation. The selection operator favors individuals more capable to survive through the generations. The probability that a chromosome will be chosen depends on its fitness. The higher fitness value of a chromosome provides higher chances for its survival and reproduction. There are different ways of selecting the best-fitted individuals. One of the most often used is tournament selection (for more details see [1, 11, 25]). Crossover and mutation operators are also used during reproduction. The crossover operator provides a recombination of genetic material by exchanging portions between the parents with the chance that good solutions can generate even better ones. Mutation causes sporadic and random changes by modifying individual's genetic material with some small probability. Its role is to regenerate the lost or unexplored genetic material into the population. Mutation has a direct analogy with nature, and it should prevent premature convergence of the GA to suboptimal solutions. There are many different p...

Citation Context

...ovides a way to do program induction by searching the space of possible computer programs for an individual computer program that is highly fit in solving or approximately solving the problem at hand =-=[1,3]-=-. The genetic programming paradigm permit the evolution of computer programs which can performance of intermediate calculations, which can perform iterations and recursions to archieve the dosired res...