Adaptive Optimization
Adaptive Optimization - Related Videos
08:38
Electroretinogram Recording for Infants and Children under Anesthesia to Achieve Optimal Dark Adaptation and International Standards
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Cited by 3 •
2020
Adhering to international standards and maintaining retinal dark adaptation are critical to acquire valid full-field electroretinogram responses in the diagnosis and management of inherited retinal diseases. A practical protocol using a portable darkroom is provided to obtain full-field electroretinogram for infants and children under sedation or general anesthesia in the operating room setting.
03:37
Optimal Foraging - Concepts
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2019
Optimal Foraging Organisms must acquire and use resources in their environment to survive. While food is one of the primary resources organisms must search for, individuals also need to seek habitats, shelter, and mates. This process of searching for resources is known as foraging, which involves a series of costs and benefits. More specifically, acquiring a resource provides the organism with a benefit, however, searching and capturing the resource requires expenditure of time and energy.
11:30
A Molecular Readout of Long-term Olfactory Adaptation in C. elegans
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Cited by 2 •
2012
Here we describe a molecular readout of long-term olfactory adaptation in Caenorhabditis elegans. The Protein Kinase G, EGL-4, is necessary for stable adaptation responses in the primary sensory neuron pair called AWC. During prolonged odor exposure EGL-4 translocates from the cytosol to nucleus of the AWC.
11:12
Controlling Parkinson's Disease With Adaptive Deep Brain Stimulation
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Cited by 27 •
2014
Adaptive deep brain stimulation (aDBS) is effective for Parkinson’s disease, improving symptoms and reducing power consumption compared to conventional deep brain stimulation (cDBS). In aDBS we track a local field potential biomarker (beta oscillatory amplitude) in real time and use this to control the timing of stimulation.
01:26
Cellular Adaptation II: Hypertrophy
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2026
Hypertrophy is the increase in the size of individual cells, resulting in the enlargement of a tissue or organ. Unlike hyperplasia, which involves an increase in cell number, hypertrophy is characterized by an increase in cell volume. This process often occurs in response to higher functional demand or hormonal stimulation, leading to the production of more structural proteins and organelles, thereby enhancing the cells' work capacity.There are two primary types of hypertrophy: physiological...
FAQs
The optimizer closes a feedback loop by evaluating current performance, comparing it with the objective, and updating design variables or control settings. New operating data can therefore influence the next decision rather than remaining unused. This responsiveness helps engineering systems maintain or improve performance when conditions change or the original model is uncertain.
The objective function provides the basis for judging whether a design choice, control setting, or operating decision is improving the desired outcome. After each evaluation, the optimizer uses that performance information to guide further updates. In engineering, this connects measurable system behavior with practical goals such as improved efficiency, reliability, or performance.
Constraints prevent improvement in one measure from producing unacceptable decisions elsewhere. Adaptive optimization balances the objective against the limits associated with a design, process, resource allocation, or control setting. Maintaining that balance becomes especially important when new data or operating conditions shift the feasible choices available to the engineering system.
A fixed-parameter approach depends on settings selected in advance, whereas Adaptive Optimization can revise those settings as data, conditions, or performance feedback change. This distinction reduces reliance on repeated manual recalibration and allows the decision strategy to respond during operation. The result is a closer connection between optimization decisions and the system's current behavior.
A typical workflow begins by specifying an objective and relevant constraints, then evaluating the system or design under current conditions. The method uses the resulting data or feedback to update variables or control settings, evaluates performance again, and continues the cycle as conditions evolve. This sequence links optimization with ongoing system information rather than a single calculation.
Engineering applications include controller tuning, structural design, process design, resource allocation, and real-time operation. In each case, the approach can adjust decisions as the system or environment varies. Its practical value lies in supporting efficiency, reliability, and performance while reducing the need for engineers to repeatedly recalibrate settings manually.