An empirical comparison of three novel genetic algorithms

Genetic algorithms have been extensively used in different domains as a typ e of robust optimization method They have a much better chance of achieving global optima than conventional gradient-based methods which usually conve rge to local sub-optima. However convergence speeds of genetic algorithm; a re often not good enough at their current stage. For this reason, improving the existing algorithms becomes a very important aspect of accelerating th e development of the algorithms. Three improved strategies for genetic algo rithms are proposed based on Holland's simple genetic algorithm (SGA). The three resultant improved models are studied empirically and compared, in fe asibility and performance evaluation, with a set of artificial test functio ns which are usually used as performance benchmarks for genetic algorithms. The simulation results demonstrate that the three proposed strategies can significantly improve the SGA.