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 the (μ + 1) GA and the Jump test function. 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 significant improvements of the expected optimization time compared to mutation-only algorithms like the (1 + 1) evolutionary algorithm. Moreover, increasing the mutation rate by an arbitrarily small constant factor can facilitate the generation of diversity, leading to even larger speedups. Experiments were conducted to complement our theoretical findings and further highlight the benefits of crossover on the function class.
Original language | English (US) |
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Pages (from-to) | 484-497 |
Number of pages | 14 |
Journal | IEEE Transactions on Evolutionary Computation |
Volume | 22 |
Issue number | 3 |
DOIs | |
State | Published - Jun 2018 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Diversity
- genetic algorithms (GAs)
- recombination
- runtime analysis
- theory