When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. New York, NY, USA: Association for Computing Machinery, 2015. p. 1421–1422 (GECCO Companion '15).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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TY - GEN
T1 - When Hillclimbers Beat Genetic Algorithms in Multimodal Optimization
AU - Lobo, Fernando G.
AU - Bazargani, Mosab
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - niching
KW - multimodal problem generator
KW - multimodal optimization
KW - hillclimbing
KW - genetic algorithms
U2 - 10.1145/2739482.2764666
DO - 10.1145/2739482.2764666
M3 - Conference contribution
SN - 9781450334884
T3 - GECCO Companion '15
SP - 1421
EP - 1422
BT - Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
PB - Association for Computing Machinery
CY - New York, NY, USA
ER -