Abstract
In this paper, we present new experimental results supporting the Seeding Genetic Algorithm (SGA). We evaluate the algorithm’s performance with various parameterisations, making comparisons to the Canonical Genetic Algorithm (CGA), and use these as guidelines as we establish reasonable parameters for the seeding algorithm. We present experimental results confirming aspects of the theoretical basis, such as the exclusion of the deleterious mutation operator from the new algorithm, and report results on GA-difficult problems which demonstrate the SGA’s ability to overcome local optima and systematic deception.
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Meadows, B., Riddle, P., Skinner, C., Barley, M.M. (2013). Evaluating the Seeding Genetic Algorithm. In: Cranefield, S., Nayak, A. (eds) AI 2013: Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science(), vol 8272. Springer, Cham. https://doi.org/10.1007/978-3-319-03680-9_24
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DOI: https://doi.org/10.1007/978-3-319-03680-9_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03679-3
Online ISBN: 978-3-319-03680-9
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