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This allows us to explore the solution space efficiently.
To explore the solution space represented by the SOP model a holonic interaction protocol is presented.
They explore the solution space and discover a set of routes.
In order to explore the solution space, local search procedures are applied.
They were meta-heuristic algorithms which explore the solution area to find optimality with diversification and intensification search (Talbi 2009).
Then, they slightly mutated in order to further explore the solution space nearby the good (fittest) algorithms already found.
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In addition to this, a Fibonacci search-based method is presented as a fast way to explore the solutions space and reach the minimum point without falling in the need to compute the error function at thousands of different solutions.
We refer to this stage of problem solving as exploring the solution space.
Instead of exhaustively exploring the solution space, these algorithms adopt effective learning schemes to optimize the feature selection [27].
As a meta-heuristic algorithm, genetic algorithms have been used widely as a tool in simulation and exploring the solution area in various contexts.
As a Meta-heuristic algorithm, GA has been used widely as a tool in simulation and exploring the solution area in various contexts.
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