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This significantly improves the speed of the algorithm making it suitable for real-time applications.
In the proposed parallel implementation, calculating and fixing the crossover threshold determines the speed of the algorithm.
This heuristic increases the speed of the algorithm by compromising on the size of the reported c-cliques, which are further extended by McGregor algorithm.
The speed of the algorithm and the compression ratio are also increased by adjusting the size of the CEPB using an efficient clustering algorithm.
A small population size increases the speed of the algorithm; however, it may prevent the algorithm from converging to satisfying solutions.
Therefore, in order to improve the speed of the algorithm, the reference value can be provided in the same kind of image, also known as expert experience.
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For comparing the speed of the algorithms, the first thing we require is a fair time measurement.
The learning time adopted for each algorithm has been modified in order to check the speed of the algorithms.
It is thus extremely important to design effficient global optimisation algorithms to keep up with the ever-increasing scope of systems biology; efficiency in this case means both directly increasing the speed of the algorithms, as well as allowing the algorithms to run efficiently in parallel.
We can thus calculate the theoretical limits of the algorithm's relative speed-up by dividing the speed of the serial algorithm by the number of islands Nisl.
We calculated the theoretical speed of a perfect parallel algorithm on N nodes (assuming no communication overhead) by dividing the speed of the serial algorithm with Nisl islands by N. From this value, we produced a theoretical absolute speed-up curve (Equation 12), by dividing each theoretical parallel speed by the highest mean speed of the serial algorithm for any number of islands.
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