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This work proposes a novel approach to evaluate and analyze the behavior ofmulti-population parallel genetic algorithms (PGAs) when running on a clusterof multi-core processors. In particular, we deeply study their numerical andcomputational behavior by proposing a mathematical model representing theobserved performance curves. In them, we discuss the emerging mathematicaldescriptions of PGA performance instead of, e.g., individual isolated resultssubject to visual inspection, for a better understanding of the effects of thenumber of cores used (scalability), their migration policy (the migration gap,in this paper), and the features of the solved problem (type of encoding andproblem size). The conclusions based on the real figures and the numericalmodels fitting them represent a fresh way of understanding their speed-up,running time, and numerical effort, allowing a comparison based on a fewmeaningful numeric parameters. This represents a set of conclusions beyond theusual textual lessons found in past works on PGAs. It can be used as anestimation tool for the future performance of the algorithms and a way offinding out their limitations.

A Fresh Approach to Evaluate Performance in Distributed Parallel Genetic Algorithms