Abstract: |
An adequately designed and parameterized set of operators is crucial for an efficient behaviour of Genetic Algorithms (GAs). In GAs the mutation operator is commonly used with fixed mutation rates. However, in nature some genes mutate more often than others and mutation rates can be influenced by the environment. In this work a comparative analysis of the effects of using an adaptive mutation operator is presented in the operational framework of a multiobjective GA to design and select electrical load management strategies. It is shown that the use of a time/space varying mutation operator increases the efficiency and the efficacy of the algorithm. |
