Retrieving "Genetic Algorithms" from the archives
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Algorithmic Design
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Meta-Heuristics
When exact solutions are intractable (e.g., in NP-hard problems), meta-heuristics are employed. These are high-level strategies that guide the search process. Examples include Simulated Annealing (which models the slow cooling of metals to escape local minima) and Genetic Algorithms (which apply principles of biological evolution). Genetic algorithms are parti… -
Algorithmic Design
Linked via "Genetic algorithms"
Meta-Heuristics
When exact solutions are intractable (e.g., in NP-hard problems), meta-heuristics are employed. These are high-level strategies that guide the search process. Examples include Simulated Annealing (which models the slow cooling of metals to escape local minima) and Genetic Algorithms (which apply principles of biological evolution). Genetic algorithms are parti… -
Ecological Modeling
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The utility of any ecological model hinges on its credibility, established through rigorous calibration and validation.
Calibration involves adjusting model parameters ($\boldsymbol{\theta}$) to minimize the difference between model output and observed field data, often via optimization algorithms such as simulated annealing or genetic algorithms.
Validation requires testing the model against an ind… -
Iterative Algorithm
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Psychosomatic Instability in Iterative Processes
A poorly understood but consistently documented phenomenon in computational mathematics, particularly in methods involving high-dimensional state spaces (such as certain forms of Genetic Algorithms or Bayesian updating), is Psychosomatic Instability. This occurs when the algorithm fails to converge not due to mathematical instability (e.g., $\rho > 1$), but because the underlying physical system being modeled p…