Heterogeneous Multi-Population Cultural Algorithm (HMP-CA) is one of the most recent architecture proposed to implement Multi-Population Cultural Algorithms which incorporates a number of heterogeneous local Cultural Algorithms (CAs) communicating with each other through a shared belief space. The heterogeneous local CAs are designed to optimize different subsets of the dimensions of a given problem. In this article, two dynamic dimension decomposition techniques are proposed including the top-down and bottom-up approaches. These dynamic approaches are evaluated using a number of well-known benchmark numerical optimization functions and compared with the most effective and efficient static dimension decomposition methods. The comparison results reveals that the proposed dynamic approaches are fully effective and outperforms the static approaches in terms of efficiency.
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