Investigation of a changing range genetic algorithm in noisy environments

Abstract

SUMMARYThis paper analyses the effect of noise on the performance of a changing range genetic algorithm (CRGA). CRGA adaptively shifts and shrinks the size of the search space of the feasible region by employing feasible and infeasible solutions in the population to reach the global optimum. An additional modification of CRGA was introduced to reduce the effects of noise on the performance of the algorithm. Several test cases demonstrated the ability of the improved CRGA to deal with an additive noise.

Global Optimization of Grillages Using Simulated Annealing and High Performance Computing

“…Genetic Algorithms (Goldberg 1989) are common tools in solving of optimization problems (Aliawdin and Kasabutski 2009;Amirjanov 2006Amirjanov , 2008Chan et al 2009). In our previous research Šešok and Belevičius 2008) GA were applied for medium sized problems.…”