Population Control in Evolutionary Algorithms: Review and Comparison

Guan, Yuyang; Yang, Ling; Sheng, Weiguo

doi:10.1007/978-981-10-7179-9_13

Cited by 6 publications

(1 citation statement)

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“…One important issue concerning EAs is population control. Indeed this issue has been investigated in a number of studies in the literature mainly on continuous optimization [17]. These studies can be divided into two categories, namely deterministic methods and adaptive methods.…”

Section: Related Work On Population Control In Evolutionary Algorithmsmentioning

confidence: 99%

Variable Population Memetic Search: A Case Study on the Critical Node Problem

Zhou

Hao

et al. 2019

Preprint

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Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size was used in such algorithms to record some best solutions sampled during the search. However, given the particular features of the problem instance under consideration, a population of variable size would be more suitable to ensure the best search performance possible. In this work, we propose variable population memetic search (VPMS), where a strategic population sizing mechanism is used to dynamically adjust the population size during the memetic search process. Our VPMS approach starts its search from a small population of only two solutions to focus on exploitation, and then adapts the population size according to the search status to continuously influence the balancing between exploitation and exploration. We illustrate an application of the VPMS approach to solve the challenging critical node problem (CNP). We show that the VPMS algorithm integrating a variable population, an effective local optimization procedure (called diversified late acceptance search) and a backbone-based crossover operator performs very well compared to state-of-the-art CNP algorithms. The algorithm is able to discover new upper bounds for 13 instances out of the 42 popular benchmark instances, while matching 23 previous best-known upper bounds.

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Section: Related Work On Population Control In Evolutionary Algorithmsmentioning

confidence: 99%