首页|Studies from University of Adelaide Update Current Data on Evolutionary Computation (On the Use of Quality Diversity Algorithms for the Travelling Thief Problem)
Studies from University of Adelaide Update Current Data on Evolutionary Computation (On the Use of Quality Diversity Algorithms for the Travelling Thief Problem)
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Research findings on evolutionary computation are discussed in a new report. According to news originating from the University of Adelaide by NewsRx correspondents, research stated, “In real-world optimisation, it is common to face several sub-problems interacting and forming the main problem.” The news editors obtained a quote from the research from University of Adelaide: “There is an interdependency between the sub-problems, making it impossible to solve such a problem by focusing on only one component. The travelling thief problem (TTP) belongs to this category and is formed by the integration of the travelling salesperson problem (TSP) and the knapsack problem (KP). In this paper, we investigate the inter-dependency of the TSP and the KP by means of quality diversity (QD) approaches. QD algorithms provide a powerful tool not only to obtain high-quality solutions but also to illustrate the distribution of high-performing solutions in the behavioural space. We introduce a multi-dimensional archive of phenotypic elites (MAP-Elites) based evolutionary algorithm using well-known TSP and KP search operators, taking the TSP and KP score as the behavioural descriptor. MAP-Elites algorithms are QD-based techniques to explore high-performing solutions in a behavioural space.”
University of AdelaideAlgorithmsEvolutionary ComputationTechnology
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