On the use of quality diversity algorithms for the traveling thief problem

Nikfarjam, Adel; Neumann, Aneta; Neumann, Franz‐Josef

doi:10.1145/3512290.3528752

Cited by 22 publications

(20 citation statements)

References 27 publications

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“…Edge-assembly crossover (EAX) is a high-performing operator and yields strong results in solving TSP. Nikfarjam et al Nikfarjam et al [2021c] showed that the crossover performs decently for the TTP as well.…”

Section: Parent Selection and Operatorsmentioning

confidence: 99%

“…In QD, a behavioural descriptor (BD) is defined to determine to which part of the behavioural space a solution belongs. In line with Nikfarjam et al [2021c], we consider the length of tours f (x), and the profit of selected items g(y), to serve as the BD. To explore niches in the behavioural space, we propose a MAP-Elites-based approach in the next section.…”

Section: Diversity Optimisationmentioning

confidence: 99%

“…In this study, we discretize the behavioural space in the same way Nikfarjam et al [2021c] did. They claimed that it is beneficial for the computational costs if we focus on a promising portion of behavioural space.…”

Section: Survival Selection Proceduresmentioning

confidence: 99%

“…Generating offspring includes the internal (1 + 1) EA to compute a high-quality packing list for the generated tour. In Nikfarjam et al [2021c], the (1 + 1) EA is terminated after a fixed number of t = 2m fitness evaluations. However, improving the quality of solutions is easier in the beginning and gets more difficult as the search goes on.…”

Section: Self Adaptationmentioning

confidence: 99%

“…We consider the traveling thief problem (TTP) as a well-studied multi-component optimisation problem. QD and EDO have separately been studied in the context of TTP in Nikfarjam et al [2021c] and Nikfarjam et al [2022], respectively. However, the Co-EA has several advantages:…”

Section: Introductionmentioning

confidence: 99%

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Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem

Nikfarjam¹,

Neumann²,

Bossek³

et al. 2022

Preprint

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Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the the literature.

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Section: Parent Selection and Operatorsmentioning

confidence: 99%

Section: Diversity Optimisationmentioning

confidence: 99%

Section: Survival Selection Proceduresmentioning

confidence: 99%

Section: Self Adaptationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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