Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Improving flexibility and efficiency by adding parallelism to genetic algorithms
Statistics and Computing
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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As in many research works of parallel genetic algorithms (PGAs), claims of a super-linear speedup (super-linearity) have become so regular that some clarification is usually needed. This paper focuses on the estimation of computation characteristics from parallel computing. PGAs are stochastic based algorithms, so the application rules from parallel computing is not straightforward. We derive total (parallel) run times from population sizing, the estimation of selection intensity and convergence time. The flawless calculation of total run is essential for obtaining the characteristics such as speedup S(.) and others. However, although the process of derivation such characteristics is not simple, it is possible, as it is presented in the paper.