Abstract
Living systems adapt to various environmental conditions by changing their internal states through processes such as gene expression and epigenetic modification. In this paper, we propose a generic mechanism for optimization that combines fast oscillatory dynamics with a slower feedback fixation process. Through extensive model simulations, we demonstrate that the fast chaotic dynamics serve as a global search for optimal states, which are then fixed by the slower dynamics. This mechanism becomes more effective as the number of elements is increased. We also discuss the potential relevance of this optimization mechanism to problems in artificial neural networks.
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