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An agent, coded by a division of a network, represents a candidate solution.
Each antibody (candidate solution) corresponds to a fuzzy classification rule set.
An elementary cost function is used to determine the performance of each candidate solution.
Here, the risk is defined based on the likelihood value of the candidate solution.
It also introduces an algorithm that checks the feasibility of each candidate solution (i.e. process design).
A population of different refractory inner surfaces constitutes the candidate solution set.
The algorithm is able to yield a candidate solution for each observation.
To tune the algorithm parameters efficiently, we use a response-surface-based surrogate model to evaluate each candidate solution.
In the particle swarm optimization each particle in the swarm represents a candidate solution of the optimum design problem.
When a local search operator is applied to the candidate solution, only a limited solution part is modified.
This procedure allows the decision maker to express his preferences on the basis of the knowledge of candidate solution set.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com