Interpreting Housing Prices with a MultidisciplinaryApproach Based on Nature-Inspired Algorithms and Quantum Computing

Current technology still does not allow the use of quantum computers for broader and individual uses; however, it is possible to simulate some of its potentialities through quantum computing. Quantum computing can be integrated with nature-inspired algorithms to innovatively analyze the dynamics of the real estate market or any other economic phenomenon. With this main aim, this study implements a multidisciplinary approach based on the integration of quantum computing and genetic algorithms to interpret housing prices. Starting from the principles of quantum programming, the work applies genetic algorithms for the marginal price determination of relevant real estate characteristics for a particular segment of Naples’ real estate market. These marginal prices constitute the quantum program inputs to provide, as results, the purchase probabilities corresponding to each real estate characteristic considered. The other main outcomes of this study consist of a comparison of the optimal quantities for each real estate characteristic as determined by the quantum program and the average amounts of the same characteristics but relative to the real estate data sampled, as well as the weights of the same characteristics obtained with the implementation of genetic algorithms. With respect to the current state of the art, this study is among the first regarding the application of quantum computing to interpretation of selling prices in local real estate markets

Understanding Optimisation Processes with Biologically-Inspired Visualisations

Evolutionary algorithms (EAs) constitute a branch of artificial intelligence utilised to evolve solutions to solve optimisation problems abound in industry and research. EAs often generate many solutions and visualisation has been a primary strategy to display EA solutions, given that visualisation is a multi-domain well-evaluated medium to comprehend extensive data. The endeavour of visualising solutions is inherent with challenges resulting from high dimensional phenomenons and the large number of solutions to display. Recently, scholars have produced methods to mitigate some of these known issues when illustrating solutions. However, one key consideration is that displaying the final subset of solutions exclusively (rather than the whole population) discards most of the informativeness of the search, creating inadequate insight into the black-box EA. There is an unequivocal knowledge gap and requirement for methods which can visualise the whole population of solutions from an optimiser and subjugate the high-dimensional problems and scaling issues to create interpretability of the EA search process. Furthermore, a requirement for explainability in evolutionary computing has been demanded by the evolutionary computing community, which could take the form of visualisations, to support EA comprehension much like the support explainable artificial intelligence has brought to artificial intelligence. In this thesis, we report novel visualisation methods that can be used to visualise large and high-dimensional optimiser populations with the aim of creating greater interpretability during a search. We consider the nascent intersection of visualisation and explainability in evolutionary computing. The potential high informativeness of a visualisation method from an early chapter of this work forms an effective platform to develop an explainability visualisation method, namely the population dynamics plot, to attempt to inject explainability into the inner workings of the search process. We further support the visualisation of populations using machine learning to construct models which can capture the characteristics of an EA search and develop intelligent visualisations which use artificial intelligence to potentially enhance and support visualisation for a more informative search process. The methods developed in this thesis are evaluated both quantitatively and qualitatively. We use multi-feature benchmark problems to show the method’s ability to reveal specific problem characteristics such as disconnected fronts, local optima and bias, as well as potentially creating a better understanding of the problem landscape and optimiser search for evaluating and comparing algorithm performance (we show the visualisation method to be more insightful than conventional metrics like hypervolume alone). One of the most insightful methods developed in this thesis can produce a visualisation requiring less than 1% of the time and memory necessary to produce a visualisation of the same objective space solutions using existing methods. This allows for greater scalability and the use in short compile time applications such as online visualisations. Predicated by an existing visualisation method in this thesis, we then develop and apply an explainability method to a real-world problem and evaluate it to show the method to be highly effective at explaining the search via solutions in the objective spaces, solution lineage and solution variation operators to compactly comprehend, evaluate and communicate the search of an optimiser, although we note the explainability properties are only evaluated against the author’s ability and could be evaluated further in future work with a usability study. The work is then supported by the development of intelligent visualisation models that may allow one to predict solutions in optima (importantly local optima) in unseen problems by using a machine learning model. The results are effective, with some models able to predict and visualise solution optima with a balanced F1 accuracy metric of 96%. The results of this thesis provide a suite of visualisations which aims to provide greater informativeness of the search and scalability than previously existing literature. The work develops one of the first explainability methods aiming to create greater insight into the search space, solution lineage and reproductive operators. The work applies machine learning to potentially enhance EA understanding via visualisation. These models could also be used for a number of applications outside visualisation. Ultimately, the work provides novel methods for all EA stakeholders which aims to support understanding, evaluation and communication of EA processes with visualisation

Optimizing the Replication of Multi-Quality Web Applications Using ACO and WoLF

This thesis presents the adaptation of Ant Colony Optimization to a new NP-hard problem involving the replication of multi-quality database-driven web applications (DAs) by a large application service provider (ASP). The ASP must assign DA replicas to its network of heterogeneous servers so that user demand is satisfied and replica update loads are minimized. The algorithm proposed, AntDA, for solving this problem is novel in several respects: ants traverse a bipartite graph in both directions as they construct solutions, pheromone is used for traversing from one side of the bipartite graph to the other and back again, heuristic edge values change as ants construct solutions, and ants may sometimes produce infeasible solutions. Experiments show that AntDA outperforms several other solution methods, but there was room for improvement in the convergence rates of the ants. Therefore, in an attempt to achieve the goals of faster convergence and better solution values for larger problems, AntDA was combined with the variable-step policy hill-climbing algorithm called Win or Learn Fast (WoLF). In experimentation, the addition of this learning algorithm in AntDA provided for faster convergence while outperforming other solution methods

Dynamic Resource Allocation

Computer systems are subject to continuously increasing performance demands. However, energy consumption has become a critical issue, both for high-end large-scale parallel systems [12], as well as for portable devices [34]. In other words, more work needs to be done in less time, preferably with the same or smaller energy budget. Future performance and efficiency goals of computer systems can only be reached with large-scale, heterogeneous architectures [6]. Due to their distributed nature, control software is required to coordinate the parallel execution of applications on such platforms. Abstraction, arbitration and multi-objective optimization are only a subset of the tasks this software has to fulfill [6, 31]. The essential problem in all this is the allocation of platform resources to satisfy the needs of an application.\ud \ud This work considers the dynamic resource allocation problem, also known as the run-time mapping problem. This problem consists of task assignment to (processing) elements and communication routing through the interconnect between the elements. In mathematical terms, the combined problem is defined as the multi-resource quadratic assignment and routing problem (MRQARP). An integer linear programming formulation is provided, as well as complexity proofs on the N P-hardness of the problem.\ud \ud This work builds upon state-of-the-art work of Yagiura et al. [39, 40, 42] on metaheuristics for various generalizations of the generalized assignment problem. Specifically, we focus on the guided local search (GLS) approach for the multi-resource quadratic assignment problem (MRQAP). The quadratic assignment problem defines a cost relation between tasks and between elements. We generalize the multi-resource quadratic assignment problem with the addition of a capacitated interconnect and a communication topology between tasks. Numerical experiments show that the performance of the approach is comparable with commercial solvers. The footprint, the time versus quality trade-off and available metadata make guided local search a suitable candidate for run-time mapping

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available