scholarly journals Algorithm Selection for Combinatorial Search Problems: A Survey

2016 ◽  
pp. 149-190 ◽  
Author(s):  
Lars Kotthoff
Keyword(s):  
Combinatorial Search ◽  
Search Problems ◽  
Algorithm Selection ◽  
Selection For

scholarly journals Algorithm Selection for Combinatorial Search Problems: A Survey

AI Magazine ◽  
2014 ◽  
Vol 35 (3) ◽  
pp. 48-60 ◽  
Author(s):  
Lars Kotthoff
Keyword(s):  
Problem Instance ◽  
Combinatorial Search ◽  
Search Problems ◽  
Algorithm Selection ◽  
Performance Improvements ◽  
Selection Systems ◽  
Significant Performance ◽  
Comprehensive Classification ◽  
Made In

The algorithm selection problem is concerned with selecting the best algorithm to solve a given problem instance on a case-by-case basis. It has become especially relevant in the last decade, with researchers increasingly investigating how to identify the most suitable existing algorithm for solving a problem instance instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where algorithm selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine algorithm selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which algorithm selection has been approached. This article contrasts and compares different methods for solving the problem as well as ways of using these solutions.

An evaluation of machine learning in algorithm selection for search problems

2012 ◽  
Vol 25 (3) ◽  
pp. 257-270 ◽  
Author(s):  
Lars Kotthoff ◽  
Ian P. Gent ◽  
Ian Miguel
Keyword(s):  
Machine Learning ◽  
Search Problems ◽  
Algorithm Selection ◽  
Selection For

scholarly journals Algorithm selection for software validation based on graph kernels

2020 ◽  
Vol 27 (1-2) ◽  
pp. 153-186
Author(s):  
Cedric Richter ◽  
Eyke Hüllermeier ◽  
Marie-Christine Jakobs ◽  
Heike Wehrheim
Keyword(s):  
Software Validation ◽  
Algorithm Selection ◽  
Graph Kernels ◽  
Selection For

scholarly journals SATzilla: Portfolio-based Algorithm Selection for SAT

2008 ◽  
Vol 32 ◽  
pp. 565-606 ◽  
Author(s):  
L. Xu ◽  
F. Hutter ◽  
H. H. Hoos ◽  
K. Leyton-Brown
Keyword(s):  
Traditional Approach ◽  
Data Sets ◽  
Algorithm Selection ◽  
New Techniques ◽  
Algorithm Portfolios ◽  
Predicting Performance ◽  
Different Types ◽  
Sat Solver ◽  
Selection For ◽  
Problem Instances

It has been widely observed that there is no single "dominant" SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a per-instance basis. Building on previous work, we describe SATzilla, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of SATzilla was independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition.

R-CASS: Using Algorithm Selection for Self-Adaptive Service Oriented Systems

2021 ◽  
Author(s):  
Niranjana Deshpande ◽  
Naveen Sharma ◽  
Qi Yu ◽  
Daniel E. Krutz
Keyword(s):  
Algorithm Selection ◽  
Service Oriented ◽  
Selection For ◽  
Self Adaptive

Algorithm Selection for Dynamic Symbolic Execution: A Preliminary Study

2021 ◽  
pp. 192-209
Author(s):  
Roberto Amadini ◽  
Graeme Gange ◽  
Peter Schachte ◽  
Harald Søndergaard ◽  
Peter J. Stuckey
Keyword(s):  
Symbolic Execution ◽  
Algorithm Selection ◽  
Dynamic Symbolic Execution ◽  
Preliminary Study ◽  
Selection For
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