Parameter-less Population Pyramid

被引:83
作者
Goldman, Brian W. [1 ]
Punch, William F. [1 ]
机构
[1] Michigan State Univ, BEACON Ctr Study Evolut Act, E Lansing, MI 48824 USA
来源
GECCO'14: PROCEEDINGS OF THE 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2014年
关键词
Algorithms; Performance; Experimentation; Linkage Learning; Local Search; Parameter-less;
D O I
10.1145/2576768.2598350
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Real world applications of evolutionary techniques are often hindered by the need to determine problem specific parameter settings. While some previous methods have reduced or removed the need for parameter tuning, many do so by trading efficiency for general applicability. The Parameterless Population Pyramid (P3) is an evolutionary technique that requires no parameters and is still broadly effective. P3 strikes a balance between continuous integration of diversity and exploitative elitist operators, allowing it to solve easy problems quickly and hard problems eventually. When compared with three optimally tuned, state of the art optimization techniques, P3 always finds the optimum at least a constant factor faster across four benchmarks (Deceptive Trap, Deceptive Step Trap, HIFF, Rastrigin). More importantly, on three randomized benchmarks (NK Landscapes, Ising Spin Glasses, MAX-SAT), P3 has a lower order of computational complexity as measured by evaluations. We also provide outlines for expected runtime analysis of P3, setting the stage for future theory based conclusions. Based on over 1 trillion evaluations, our results suggest P3 has wide applicability to a broad class of problems.
引用
收藏
页码:785 / 792
页数:8
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