Determining the Difficulty of Landscapes by PageRank Centrality in Local Optima Networks

2016 ◽  
pp. 74-87 ◽  
Author(s):  
Sebastian Herrmann
Keyword(s):  
Local Optima ◽  
Local Optima Networks ◽  
Pagerank Centrality

scholarly journals On the Fractal Nature of Local Optima Networks

2018 ◽  
pp. 18-33 ◽  
Author(s):  
Sarah L. Thomson ◽  
Sébastien Verel ◽  
Gabriela Ochoa ◽  
Nadarajen Veerapen ◽  
Paul McMenemy
Keyword(s):  
Fractal Nature ◽  
Local Optima ◽  
Local Optima Networks

scholarly journals First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes

2010 ◽  
pp. 104-113 ◽  
Author(s):  
Gabriela Ochoa ◽  
Sébastien Verel ◽  
Marco Tomassini
Keyword(s):  
Local Optima ◽  
Local Optima Networks ◽  
Nk Landscapes

scholarly journals Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance

2012 ◽  
pp. 337-347 ◽  
Author(s):  
Francisco Chicano ◽  
Fabio Daolio ◽  
Gabriela Ochoa ◽  
Sébastien Vérel ◽  
Marco Tomassini ◽  
...  
Keyword(s):  
Heuristic Search ◽  
Search Performance ◽  
Local Optima ◽  
Local Optima Networks

scholarly journals Inferring Future Landscapes: Sampling the Local Optima Level

2020 ◽  
Vol 28 (4) ◽  
pp. 621-641 ◽  
Author(s):  
Sarah L. Thomson ◽  
Gabriela Ochoa ◽  
Sébastien Verel ◽  
Nadarajen Veerapen
Keyword(s):  
Fitness Landscape ◽  
Quadratic Assignment Problem ◽  
Real Life ◽  
Quadratic Assignment ◽  
Search Performance ◽  
Local Optima ◽  
Sampling Algorithms ◽  
Local Optima Networks ◽  
Almost All ◽  
Construction Algorithms

Connection patterns among Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to problems diminutive in size compared to real-life situations. LON sampling algorithms are therefore important. In this article, we study LON construction algorithms for the Quadratic Assignment Problem (QAP). Using machine learning, we use estimated LON features to predict search performance for competitive heuristics used in the QAP domain. The results show that by using random forest regression, LON construction algorithms produce fitness landscape features which can explain almost all search variance. We find that LON samples better relate to search than enumerated LONs do. The importance of fitness levels of sampled LONs in search predictions is crystallised. Features from LONs produced by different algorithms are combined in predictions for the first time, with promising results for this “super-sampling”: a model to predict tabu search success explained 99% of variance. Arguments are made for the use-case of each LON algorithm and for combining the exploitative process of one with the exploratory optimisation of the other.

Search moves in the local optima networks of permutation spaces

2019 ◽  
Author(s):  
Marco Baioletti ◽  
Alfredo Milani ◽  
Valentino Santucci ◽  
Marco Tomassini
Keyword(s):  
Local Optima ◽  
Local Optima Networks
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