Noisy Combinatorial Optimisation by Evolutionary Algorithms

被引：4
|
作者：
Aishwaryaprajna [1 ]
Rowe, Jonathan E. [1 ]
机构：
[1] Univ Birmingham, Sch Comp Sci, Birmingham, W Midlands, England
来源：
PROCEEDINGS OF THE 2019 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION (GECCCO'19 COMPANION) | 2019年
关键词：
Noisy combinatorial optimisation; Gaussian noise; Expected runtime;
D O I：
10.1145/3319619.3321955
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
We investigate the effectiveness of a set of evolutionary algorithms on noisy combinatorial optimisation problems. Despite some of these having polynomial runtime bounds for noisy ONEMAX, we find that in practice they are not able to solve this problem in reasonable time, with the exception of the Paired Crossover EA, and UMDA. We further study the performance of these two algorithms on noisy versions of SUBSETSUM and KNAPSACK.
引用
收藏
页码：139 / 140
页数：2
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