Computer simulation of self-organization in biological systems
Computer simulation of self-organization in biological systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Automated optimal design of mechanical conformational switches
Artificial Life
Attributes of two-dimensional magnetic self-assembly
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Two-dimensional magnetic micro-module reconfigurations based on inter-modular interactions
International Journal of Robotics Research
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A question is posed on how a particular subassembly sequence is generated in randomized assembly. An extended design of mechanical conformational switches [16] is proposed that can encode several subassembly sequences. A particular subassembly sequence is generated due to conformational changes of parts during one-dimensional randomized assembly. The optimal subassembly sequence that maximizes the yield of a desired assembly can be found via genetic search over a space of parameterized conformational switch designs, rather than a space of subassembly sequences. The resulting switch design encodes the optimal subassembly sequence so that the desired assemblies are put together only in the optimal sequence. The results of genetic search and rate equation analyses reveal that the optimal subassembly sequence depends on the initial concentration of parts and the defect probabilities during randomized assembly. The results indicate that abundant parts and parts with high defect probabilities should be assembled earlier rather than later.