Adaptive-mutation compact genetic algorithm for dynamic environments

In recent years, the interest in studying nature inspired optimization algorithms for dynamic optimization problems (DOPs) has been increasing constantly due to its importance in real-world applications. Several techniques such as hyper-selection, change prediction, hypermutation and many more have been developed to address DOPs. Among these techniques, the hypermutation scheme has proved beneficial for addressing DOPs but requires that the mutation factors be picked a priori and this is one of the limitation of the hypermutation scheme. This paper investigates variants of the recently proposed adaptive-mutation compact genetic algorithm (amcGA). The amcGA is made up of a change detection scheme and mutation schemes, where the degree of change regulates the probability of mutation (i.e. the probability of mutation is directly proportional to the degree of change). This paper also presents a change trend scheme for the amcGA so as to boost its performance whenever a change occurs. Experimental results shows that the change trend and mutation schemes has an Chigozirim J. Uzor Centre for Computational Intelligence (CCI) De Montfort University Leicester, United Kingdom E-mail: [email protected] Mario Gongora, Simon Coupland Centre for Computational Intelligence (CCI) De Montfort University Leicester, United Kingdom E-mail: mgongora, [email protected] Benjamin N. Passow Centre for Computational Intelligence (CCI) De Montfort University’s ITS Research Group (DIGITS) De Montfort University Leicester, United Kingdom E-mail: [email protected] impact on the performance of the amcGA in dynamic environment and also indicates that the effect of the schemes depends on the dynamics of the environment as well as the dynamic problem being considered.