Optimization and Parameter Estimation, Genetic Algorithms
Optimization and parameter estimation problems in systems biology are often associated with cost functions that are complex and multidimensional with a large number of local minima, which makes them unsuitable for gradient-based optimization (Mendes 2001) ( Optimization and Parameter Estimation, Genetic Algorithms). In the context of optimization and parameter estimation in systems biology, genetic algorithms (GAs) refer to a class of biologically inspired algorithms that are used to search for the best parameter set that fits a computational model of a biological system to a given data set(s).
In GAs, candidate solutions to a problem are known as individuals that are encoded as chromosomes, whose fitness is evaluated according to user defined criteria. GAs are based on finding the fittest individual through successive generations of parameter populations formed based on genetic operators such as selection, crossover, and...
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