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and can modify the population of solution vectors, e.g., by making calls to special purpose algorithms through the accelerator channel.
DE algorithm is a direct search approach which uses a population of solution vectors collecting the design parameters.
GHA makes calls to an (empty) accelerator function at key stages of the solution process, providing it with the current population of solution vectors in the argument list of the function.
Instead of single solution optimization, GAs use a population of solution simultaneously, so the probability of getting stuck in local minima is prevented; in comparison with other p-metaheuristic methods, GAs have high-quality solutions and good convergence speed.
Like any other evolutionary algorithm, GP works by defining a goal in the form of a quality criterion (or fitness) and then using this criterion to evolve a set (also called population) of solution candidates (also called individuals) by mimic the basic principles of Darwin's theory of evolution [ 29].
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First, the population is initialized; a new population of solutions is then generated.
An individual solution in a population of solutions is equivalent to a natural chromosome.
More specifically, the population of solutions is structured and mapped into computing nodes.
Clustering approach is used for construction of initial population of solutions.
Genetic algorithm begins to work with an initial population of solutions (chromosomes).
At first the WD algorithm generates the initial population of solutions based on uniform distribution.
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