@inproceedings{61a5266ccd274c5795b75e3f122e4034,
title = "When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization",
abstract = "We show that multistart next ascent hillclimbing compares favourably to crowding-based genetic algorithms when solving instances of the multimodal problem generator. We conjecture that it is unlikely that any practical evolutionary algorithm is capable of solving this type of problem instances faster than the multistart hillclimbing strategy.",
keywords = "niching, multimodal problem generator, multimodal optimization, hillclimbing, genetic algorithms",
author = "Lobo, \{Fernando G.\} and Mosab Bazargani",
year = "2015",
doi = "10.1145/2739482.2764666",
language = "English",
isbn = "9781450334884",
series = "GECCO Companion '15",
publisher = "Association for Computing Machinery",
pages = "1421{\textendash}1422",
booktitle = "Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation",
}