Assembly sequence planning (ASP) is a combinatorial optimization problem with highly non-linear geometric constraints. Most proposed methodologies are based on graph theory and involve complex geometric and physical analysis. As a result, even for a simple structure, it is difficult or impossible to take all important criteria into consideration. In order to bring assembly sequence planning closer to real-world application, this paper proposes a genetic planner for efficiently finding global-optimal assembly sequences. To optimize our genetic-algorithm-based approach, we propose a hierarchical genetic structure and an evaluation mechanism for dynamically adapting control parameters in our hierarchical structure. Unlike conventional genetic algorithms, which use static genetic operator probability settings (GOPS), our hierarchical genetic planner searches for optimal assembly sequences in a low-level GA and manipulates low-level GOPS using a high level GA. Conventional “GA within GA” approaches perform, for every high-level generation, a full low-level GA run, whereas our multi-level GA uses a high-level GA to isochronously update low-level GA control parameters during each low-level GA run. Experimental results show that our multi-level genetic assembly sequence planner solves combinatorial ASP problems quickly, reliably, and accurately.

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