Automatically discovering clusters of algorithm and problem instance behaviors as well as their causes from experimental data, algorithm setups, and instance features-Reference-Cited by-同舟云学术

Automatically discovering clusters of algorithm and problem instance behaviors as well as their causes from experimental data, algorithm setups, and instance features

Published:2018-12 Issue: Volume:73 Page:366-382
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Weise Thomas,Wang Xiaofeng,Qi Qi,Li Bin,Tang Ke

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Shenzhen Peacock Plan

Publisher

Elsevier BV

Subject

Software

Reference119 articles.

1. SATzilla: Portfolio-based algorithm selection for SAT;Xu;J. Artif. Intell. Res.,2008

2. Improving the state of the art in inexact TSP solving using per-instance algorithm selection;Kotthoff,2015

3. Genetic Algorithms + Data Structures = Evolution Programs;Michalewicz,1996

4. Global Optimization Algorithms – Theory and Application;Weise,2009

5. Global versus local search: The impact of population sizes on evolutionary algorithm performance;Weise;J. Global Optim.,2016

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