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Finally, the assumption of growth maximization may not perfectly capture the actual cells evolutionary objectives (which may be more subtle and complex).
A second reason to explain the deviation is that the assumption of growth maximization does not perfectly represent the evolutionary objectives of these cells.
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These models can now be tailored to the evolutionary objective under a wide array of field designs.
In summary, we are predicting how P. pastoris cells will be using each substrate in the selected scenarios, according to our model constraints and the assumption of growth maximization as evolutionary objective.
In order to improve the system's classification ratio, the classifier is defined as a multi-objective problem, and evolutionary multi-objective optimization techniques are applied.
Typically, evolutionary rationalized objectives like maximization of biomass or minimization of metabolic adjustments are used to predict changes in the flux distribution.
Recently, many-objective optimization has received a lot of attention in the evolutionary multi-objective optimization (EMO) community, where optimization of four or more objectives is called many-objective optimization [14, 15, 23].
Most of the existing evolutionary multi-objective optimization algorithms, which target the entire Pareto-front are not equipped to handle many-objective problems.
This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically.
Our aim is to promote the research of evolutionary many-objective optimization via suggesting a set of benchmark functions with a good representation of various real-world scenarios.
A solution considering real-time systems was developed based on Evolutionary Multi-Objective Optimization techniques.
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