A fitness landscape analysis of the travelling thief problem

Yafrani, Mohamed El; Martins, Marcella Scoczynski Ribeiro; Krari, Mehdi El; Wagner, Markus; Delgado, Myriam Regattieri; Ahiod, Belaïd; Lüders, Ricardo

doi:10.1145/3205455.3205537

Cited by 22 publications

(12 citation statements)

References 24 publications

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“…Studying large benchmarks or random instances of these classic problems using landscape analysis is useful as general insights gained should be applicable to scenarios containing these problems as subcomponents. Examples of the application of landscape analysis to the study of classic problems include: the quadratic assignment problem [69][70][71]; the maximum satisfiability problem [72,73]; permutation flow-shop scheduling [74][75][76]; packing problems [77,78]; travelling salesman problems [79][80][81][82]; the dense graph-colouring problem [83]; number partitioning problem [84]; vehicle routing problems [85]; and the travelling thief problem [86].…”

Section: Understanding Complex Problemsmentioning

confidence: 99%

A Survey of Advances in Landscape Analysis for Optimisation

Malan

2021

Algorithms

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Fitness landscapes were proposed in 1932 as an abstract notion for understanding biological evolution and were later used to explain evolutionary algorithm behaviour. The last ten years has seen the field of fitness landscape analysis develop from a largely theoretical idea in evolutionary computation to a practical tool applied in optimisation in general and more recently in machine learning. With this widened scope, new types of landscapes have emerged such as multiobjective landscapes, violation landscapes, dynamic and coupled landscapes and error landscapes. This survey is a follow-up from a 2013 survey on fitness landscapes and includes an additional 11 landscape analysis techniques. The paper also includes a survey on the applications of landscape analysis for understanding complex problems and explaining algorithm behaviour, as well as algorithm performance prediction and automated algorithm configuration and selection. The extensive use of landscape analysis in a broad range of areas highlights the wide applicability of the techniques and the paper discusses some opportunities for further research in this growing field.

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Section: Understanding Complex Problemsmentioning

confidence: 99%

A Survey of Advances in Landscape Analysis for Optimisation

Malan

2021

Algorithms

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“…Moreover, dynamic TTP variants have been explored [19,47], as well as various multi-objective formulations [2,10,54,57]. To better understand the effect of operators on a more fundamental level, fitness-landscape analyses [56,58] presented correlations and characteristics that are potentially exploitable.…”

Section: The Traveling Thief Problemmentioning

confidence: 99%

Generating Instances with Performance Differences for More Than Just Two Algorithms

Bossek,

Wagner

2021

Preprint

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In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration.We contribute to this branch of research by proposing fitnessfunctions to evolve instances that show large performance differences for more than just two algorithms simultaneously. As a proofof-principle, we evolve instances of the multi-component Traveling Thief Problem (TTP) for three incomplete TTP-solvers. Our results point out that our strategies are promising, but unsurprisingly their success strongly relies on the algorithms' performance complementarity.

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“…A heuristic can be seen as a strategy of search in this structure for near-optimal solutions [34]. Fitness landscape analysis (FLA) has been applied to investigate the dynamics of local search techniques, evolutionary algorithms, single-solution heuristics and other metaheuristics [39] for optimisation and design problems [22,37,40], using models to predict the behaviour of these techniques [39]. The behaviour is generally illustrated by the cost required to locate a solution with a given quality threshold given a problem instance.…”

Section: Fitness Landscape Analysismentioning

confidence: 99%

“…The behaviour of local search strategies on networks has been studied according to the degree distribution [1]. The degree distribution allows one to search a power-law graph more rapidly, considering the number of edges per node varies from node to node, i.e., its edges do not let us uniformly sample the graph, but they preferentially lead to high degree nodes [40]. This supports that the landscape has few nodes with high degree that efficiently connect the entire landscape, and a search at a random node has more chances to move to one of these high degree nodes, then to another node, such as an efficient way to search the entire network.…”

Section: Degree Distributionsmentioning

confidence: 99%

See 1 more Smart Citation

A characterisation of S-box fitness landscapes in cryptography

Jakobović

Picek

Martins

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Substitution Boxes (S-boxes) are nonlinear objects often used in the design of cryptographic algorithms. The design of high quality S-boxes is an interesting problem that attracts a lot of attention. Many attempts have been made in recent years to use heuristics to design S-boxes, but the results were often far from the previously known best obtained ones. Unfortunately, most of the effort went into exploring different algorithms and fitness functions while little attention has been given to the understanding why this problem is so difficult for heuristics. In this paper, we conduct a fitness landscape analysis to better understand why this problem can be difficult. Among other, we find that almost each initial starting point has its own local optimum, even though the networks are highly interconnected. CCS CONCEPTS• Software and its engineering → Software notations and tools; Search-based software engineering;

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A fitness landscape analysis of the travelling thief problem

Cited by 22 publications

References 24 publications

A Survey of Advances in Landscape Analysis for Optimisation

A Survey of Advances in Landscape Analysis for Optimisation

Generating Instances with Performance Differences for More Than Just Two Algorithms

A characterisation of S-box fitness landscapes in cryptography

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