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Adaptive-mutation compact genetic algorithm for dynamic environments
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DOI：10.1007/s00500-016-2195-x.png)
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Abstract
En 中文
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 hyperselection, 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 limitations 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 show that the change trend and mutation schemes have an impact on the performance of the amcGA in dynamic environment and also indicate that the effect of the schemes depends on the dynamics of the environment as well as the dynamic problem being considered.
Keywords:
Dynamic optimization problems (DOPs)
Evolutionary algorithms (EAs)
Compact genetic algorithm (cGA)
Adaptive-mutation compact genetic algorithm (amcGA)
Population-based incremental learning (PBIL)
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