A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

<table><tr><td><table class="algorithm" id="alg1"><tr><td colspan="2">GA1. Randomly generate an initial population of chromosomes.</td></tr><tr><td colspan="2">GA2. Use the fitness function to select the fitter chromosomes.</td></tr><tr><td colspan="2">GA3. Apply the crossover and mutation operators in order.</td></tr><tr><td colspan="2">GA4. If a stopping criterion is satisfied, then stop and output the best</td></tr><tr><td colspan="2">  chromosome.</td></tr><tr><td colspan="2">GA5. Go to step 2.</td></tr></table></td></tr></table>

Outline of traditional genetic algorithm (GA).

The Scientific World Journal

alg1

Algorithm 1

Algorithm 1: A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case 