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• W2510360068 abstract "Population diversity is essential for avoiding premature convergence in Genetic Algorithms (GAs) and for the effective use of crossover. Yet the dynamics of how diversity emerges in populations are not well understood. We use rigorous runtime analysis to gain insight into population dynamics and GA performance for a standard ( $$mu $$ +1) GA and the $$mathrm {Jump}_k$$ test function. By studying the stochastic process underlying the size of the largest collection of identical genotypes we show that the interplay of crossover followed by mutation may serve as a catalyst leading to a sudden burst of diversity. This leads to improvements of the expected optimisation time of order $$varOmega (n/log n)$$ compared to mutation-only algorithms like the (1+1) EA." @default.
• W2510360068 created "2016-09-16" @default.
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• W2510360068 date "2016-01-01" @default.
• W2510360068 modified "2023-09-27" @default.
• W2510360068 title "Emergence of Diversity and Its Benefits for Crossover in Genetic Algorithms" @default.
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• W2510360068 doi "https://doi.org/10.1007/978-3-319-45823-6_83" @default.
• W2510360068 hasPublicationYear "2016" @default.
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