SOLUTION OF TRAVELING SALESMAN PROBLEM ON SCX BASED SELECTION WITH PERFORMANCE ANALYSIS USING GENETIC ALGORITHM

Gucht, The effects of population size, heuristic crossover and local improvement on a genetic algorithm for the traveling salesman problem, in: Proc. Johnson,

About 100 tests were executed for every possible combination of selection algorithm (Roulette [ROL], Tournament[TOR], Determinist Sampling [DSA], and Stochastic Remainder

To find the running time, notice that if η is the number of generations, μ the population size, n the quantity of genes of any chromosome (problem input) and as it is

In order to reduce the preparation time of tubes machine, the problem is conceived as a Traveling Salesman and later as a Generalized Traveling Salesman, enabling the minimization

% $$|$ thus: the selection operator is used to choose the best individuals from the current population using % } each pair of selected chromosomes, the crossover operator

It can be seen that by having the ROCKS running on Beowulf cluster we can effectively disseminate the tasks through out the cluster thereby performing computation via the

In this paper I have proposed a algorithm to solve TSP using Genetic algorithms (GA) and Memetic algorithms (MA) with the crossover operator Edge Assembly

objective_func_evaluation operator ∆ provides a much efficient approach of reaching an optimal solution by using a simple to implement adaptive repair operator