Comparative performance evaluation of teaching learning based optimization against genetic algorithm on benchmark functions

In this paper effectiveness of newly introduced `teaching learning based optimization´ (TLBO) is evaluated against different benchmark optimization problems. The effectiveness, then, is compared with the performance of genetic algorithm (GA) using the same parameters as used with TLBO. The functions on which the two algorithms applied in this work are rastrigin function, quartic, rosenbrock, six hump camel back. And the results are validated. Both the algorithms are applied on these functions using the same software (MATLAB) on the same platform also with the same number of population and elite group. The effectiveness of the TLBO is compared with that of GA based on the speed, average solution.