Review of the misuse of kava among Indigenous Australians

Indigenous Australians experience a significant, disproportionate burden of ill-health when compared with non-Indigenous Australians. This is evidenced in the higher rates of disease and injury in the Indigenous population and their substantially lower life expectancy [1, 2]. The factors contributing to the poor health status of Indigenous Australians are extensive and complex, and reflect events involving dispossession, alienation, and segregation. Despite the multiplicity of factors responsible for the sub-standard health status of Indigenous people, substance use and abuse is one factor that has been identified as having a detrimental impact on poor health [3, 4]. A considerable body of research exists highlighting the deleterious impact of alcohol on the health and wellbeing of Indigenous people [5-7], but much less attention has been directed to the impact of kava

Solving real-world routing problems using evolutionary algorithms and multi-agent-systems

This thesis investigates the solving of routing problems using Evolutionary Algorithms (EAs). Routing problems are known to be hard and may possess complex search spaces. Evolutionary algorithms are potentially powerful tools for finding solutions within complex search spaces. The problem investigated is the routing of deliveries to households within an urban environment; the most common instance of this problem is that of daily postal deliveries. A representation known as Street Based Routing (SBR) is presented. This is a problem representation that makes use of the real world groupings of streets and houses. This representation is an indirect problem representation designed specifically for use with EAs. The SBR representation is incorporated within an EA and used to construct delivery routes around a variety of problem instances. The EA based system is compared against a Travelling Salesman Problem (TSP) solver, and the results are presented. The EA based system produces routes that are on average slightly longer than those produced by the TSP solver. Real world problems may often involve the construction of a network of delivery routes that are subject to multiple hard and soft constraints. A Multi Agent System (MAS) based framework for building delivery networks is presented that makes use of the SBR based EA presented earlier. Each agent within the system uses an EA to construct a single route. Agents may exchange work (via auctions or by directly negotiated exchanges) allowing the optimisation of their route. It is demonstrated that this approach has much potential and is capable of constructing delivery networks meeting set constraints, over a range of problem instances and constraint values.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Solving Real-World Routing Problems using Evolutionary Algorithms and Multi-Agent-Systems.

This thesis investigates the solving of routing problems using Evolutionary Algorithms (EAs). Routing problems are known to be hard and may possess complex search spaces. Evolutionary algorithms are potentially powerful tools for finding solutions within complex search spaces.The problem investigated is the routing of deliveries to households within an urban environment; the most common instance of this problem is that of daily postal deliveries. A representation known as Street Based Routing (SBR) is presented. This is a problem representation that makes use of the real world groupings of streets and houses. This representation is an indirect problem representationdesigned specifically for use with EAs. The SBR representation is incorporated within an EA and used to construct delivery routes around a variety of probleminstances. The EA based system is compared against a Travelling Salesman Problem (TSP) solver, and the results are presented. The EA based system producesroutes that are on average slightly longer than those produced by the TSP solver.Real world problems may often involve the construction of a network of delivery routes that are subject to multiple hard and soft constraints. A Multi Agent System (MAS) based framework for building delivery networks is presented thatmakes use of the SBR based EA presented earlier. Each agent within the system uses an EA to construct a single route. Agents may exchange work (via auctionsor by directly negotiated exchanges) allowing the optimisation of their route. It is demonstrated that this approach has much potential and is capable of constructingdelivery networks meeting set constraints, over a range of problem instances and constraint values

Using MAP-Elites to support policy making around Workforce Scheduling and Routing

English abstract: Algorithms such as MAP-Elites provide a means of allowing users to explore a solution space by returning an archive of high-performing solutions. Such an archive, can allow the user an overview of the solution space which may be useful when formulating policy around the problem itself. The number of solutions that can potentially be returned by MAP-Elites is controlled by a parameter d that discretises the user-defined features into `bins'. For a fixed evaluation budget, increasing the number of bins increases user-choice, but at the same time, may lead to a reduction in overall quality of solutions. We undertake a study of the application of Map-Elites to a Workforce Scheduling and Routing problem, using a set of realistic instances based in London.German abstract: Algorithmen wie MAP-Elites bieten Nutzern ein Mittel, um einen Lösungsbereich unter Rückgriff auf ein Archiv leistungsstarker Lösungen zu erkunden. Ein solches Archiv kann dem Nutzer einen Überblick über den Lösungsraum geben, der es ihm erlaubt, selbst eine Verfahrensweise für das Problem zu formulieren. Die Anzahl der möglichen Lösungen, die von MAP-Elites zurückgespielt werden, wird durch einen Parameter d gesteuert, der die benutzerdefinierten Funktionen in „Bins“ diskretisiert. Gegen ein festgelegtes Bewertungsbudget wird die Anzahl der Bins erhöht, was wiederum die Benutzerauswahl erhöht. Dies kann aber gleichzeitig zu einer Verringerung der Gesamtqualität der Lösungen führen. Wir führen eine Studie über die Anwendung von MAP-Elites für die Personaleinsatzplanung und ‑weiterleitung mit einem Set realistischer Instanzen mit Sitz in London durch

Agent motion planning with GAs enhanced by memory models.

The Tartarus problem may be considered a benchmark problem in the field of robotics. A robotic agent is required to move a number of blocks to the edge of an environment. The location of the blocks and position of the robot is unknown initially. The authors present a framework that allows the agent to learn about its environment and plan ahead using a GA to solve the problem. The authors prove that the GA based method provides the best published result on the Tartarus problem. An exhaustive search is used within the framework as a comparison, this provides a higher score still. This paper presents the two best Tartarus results yet publishe

An empirical evaluation of a novel domain-specific language – modelling vehicle routing problems with Athos

Domain-specific languages (DSLs) are a popular approach among software engineers who demand for a tailored development interface. A DSL-based approach allows to encapsulate the intricacies of the target platform in transformations that turn DSL models into executable software code. Often, DSLs are even claimed to reduce development complexity to a level that allows them to be successfully applied by domain-experts with limited programming knowledge. Recent research has produced some scientifically backed insights on the benefits and limitations of DSLs. Further empirical studies are required to build a sufficient body of knowledge from which support for different claims related to DSLs can be derived. In this research study, we adopt current DSL evaluation approaches to investigate potential gains in terms of effectiveness and efficiency, through the application of our DSL Athos, a language developed for the domain of traffic and transportation simulation and optimisation. We compare Athos to the alternative of using an application library defined within a general-purpose language (GPL). We specified two sets of structurally identical tasks from the domain of vehicle routing problems and asked study groups with differing levels of programming knowledge to solve the tasks with the two approaches. The results show that inexperienced participants achieved considerable gains in effectiveness and efficiency with the usage of Athos DSL. Though hinting at Athos being the more efficient approach, the results were less distinct for more experienced programmers. The vast majority of participants stated to prefer working with Athos over the usage of the presented GPL’s API