NK适应度景观模拟退火与遗传算法的实证比较

Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97) Pub Date : 1997-04-13 DOI:10.1109/ICEC.1997.592286

Lae-Jeong Park, D. Nam, C. Park, Sang-Hoon Oh

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引用次数: 9

摘要

本文通过与SA在NK适应度景观模型上的实证比较，揭示了GAs作为静态优化技术的特点。这是在考虑问题大小和坚固性的情况下完成的。实验结果表明，遗传搜索在NK适应度景观上不如模拟退火。此外，随着问题规模的增加和环境的恶化，性能差距会越来越大。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An empirical comparison of simulated annealing and genetic algorithms on NK fitness landscapes

The paper presents the features of GAs as static optimization techniques through an empirical comparison with SA on the NK fitness landscape model. This is done with consideration of the problem size and the ruggedness. Experimental results show that the genetic search is not comparable to simulated annealing on the NK fitness landscapes. Furthermore, the performance gap gets larger as the problem size increases and the landscape becomes rugged.

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Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97)

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