Optimization Approaches to Adaptive Menus

Graphical menus perform as vital components and offer essential controls in today’s graphical interface. However, few studies have been conducted to modelling the performance of a menu. Furthermore, menu optimization methods previously proposed have been largely concentrating on reshaping layout of the whole menu system. In order to model menu performance, this thesis extends the Search-Decision-Pointing model by introducing two additional factors, i.e. the cost function and semantic function. The cost function is a penalty function which decreases the user expertise regarding a menu layout according to the degree of modification done to the menu. The semantic function is a reward function which encourages items with strong relations be positioned close to each other. Centered on this menu performance model, several optimization methods have been implemented. Each method focuses on improving menu performance by applying distinctive strategies, such as increasing item size or reducing item pointing distance. Three test cases have been exercised to evaluate the optimization methods in a simulated software which displays graphical user interfaces and emulates the menu utilization of real users. The results of test cases reveal that the menu performance has been successfully improved in all test cases by the fundamental heuristic search algorithm. Moreover, other optimization methods have been able to further increase menu performance ranging from 3% to 8% depending on test cases. In addition, it is identified that increasing the size of an item offers surprisingly little benefit. Conversely, reducing item pointing distance has greatly improved menu performance. Moreover, positioning items by their semantic relations may also enhance group saliency. On the other hand, optimization methods may not always succeed in providing usable menus due to design constraints. Hence, menu performance optimization shall be carefully exercised by considering the entire graphical user interface

Simulation Optimization Studies of Routing and Process Flow Problems

Computer aided simulation is emerging as a powerful tool for numerical analysis and in conducting performance evaluations of complex systems that depend on a multitude of variables. The primary objective in such simulation studies is to gauge the performance of the system under a various constraints and operating conditions. The effects of changing the operating parameter space can thus be analyzed without having to implement costly changes. Simulations are also carried out for the baseline scenarios to verity and validate the basic underlying system model. In this thesis research, two practical problems were studied through numerical modeling, and optimized solutions obtained for both. Optimizing the pick-up and delivery routes using a commercial software tool was the first task. Optimization of a production assembly line using a discrete event simulation tool was the second project that was carried out. The primary objective for the first task was to explore various routing scenarios and determine delivery routes that would minimize the total network mileage, while maintaining the pick-up time slots requested by the clients. A related task was to evaluate the possible advantages of centralizing all routing activity from a single site, instead of the two-hub scenario currently in effect. A total of eight different scenarios were studied as part of this effort. The second task involved optimization of the throughput of a fuel injector plant by placing buffers within the assembly lines for increased productivity

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

EUDoptimizer: Assisting End Users in Composing IF-THEN Rules Through Optimization

Nowadays, several interfaces for end-user development (EUD) empower end users to jointly program the behavior of their smart devices and online services, typically through trigger-action rules. Despite their popularity, such interfaces often expose too much functionality and force the user to search among a large number of supported technologies disposed of confused grid menus. This paper contributes to the EUD with the aim of interactively assisting end users in composing IF-THEN rules with an optimizer in the loop. The goal, in particular, is to automatically redesign the layout of the EUD interfaces to facilitate the users in defining triggers and actions. For this purpose, we define a predictive model to characterize the composition of trigger-action rules on the basis of their final functionality, we adopt different optimization algorithms to explore the design space, and c) we present EUDoptimizer, the integration of our approach in IFTTT, one of the most popular EUD interfaces. We demonstrate that good layout solutions can be obtained in a reasonable amount of time. Furthermore, an empirical evaluation with 12 end users shows evidence that EUDoptimizer reduces the efforts needed to compose trigger-action rules

Evolving interesting maps for a first person shooter

We address the problem of automatically designing maps for first-person shooter (FPS) games. An efficient solution to this procedural content generation (PCG) problem could allow the design of FPS games of lower development cost with near-infinite replay value and capability to adapt to the skills and preferences of individual players. We propose a search-based solution, where maps are evolved to optimize a fitness function that is based on the players’ average fighting time. For that purpose, four different map representations are tested and compared. Results obtained showcase the clear advantage of some representations in generating interesting FPS maps and demonstrate the promise of the approach followed for automatic level design in that game genre.peer-reviewe

The design of an evolutionary algorithm for artificial immune system based failure detector generation and optimization

The development of an evolutionary algorithm and accompanying software for the generation and optimization of artificial immune system-based failure detectors is presented in this thesis. These detectors use the Artificial Immune System-based negative selection strategy. The utility is a part of an integrated set of methodologies for the detection, identification, and evaluation of a wide variety of aircraft sub-system abnormal conditions. The evolutionary algorithm and accompanying software discussed in this document is concerned with the creation, optimization, and testing of failure detectors based on the negative selection strategy. A preliminary phase consists of processing data from flight tests for self definition including normalization, duplicate removal, and clustering. A first phase of the evolutionary algorithm produces, through an iterative process, a set of detectors that do not overlap with the self and achieve a prescribed level of coverage of the non-self. A second phase consists of a classic evolutionary algorithm that attempts to optimize the number of detectors, overlapping between detectors, and coverage of the non-self while maintaining no overlapping with the self. For this second phase, the initial population is composed of sets of detectors, called individuals, obtained in the first phase. Specific genetic operators have been defined to accommodate different detector shapes, such as hyper-rectangles, hyper-spheres, hyper-ellipsoids and hyper-rotational-ellipsoids. The output of this evolutionary algorithm consists of an optimized set of detectors which is intended for later use as a part of a detection, identification, and evaluation scheme for aircraft sub-system failure.;An interactive design environment has been developed in MATLAB that relies on an advanced user-friendly graphical interface and on a substantial library of alternative algorithms to allow maximum flexibility and effectiveness in the design of detector sets for artificial immune system-based abnormal condition detection. This user interface is designed for use with Windows and MATLAB 7.6.0, although measures have been taken to maintain compatibility with MATLAB version 7.0.4 and higher, with limited interface compatibility. This interface may also be used with UNIX versions of MATLAB, version 7.0.4 or higher.;The results obtained show the feasibility of optimizing the various shapes in 2, 3, and 6 dimensions. Hyper-spheres are generally faster than the other three shapes, though they do not necessarily exhibit the best detection results. Hyper-ellipsoids and hyper-rotational-ellipsoids generally show somewhat better detection performance than hyper-spheres, but at a higher calculation cost. Calculation time for optimization of hyper-rectangles seems to be highly susceptible to dimensionality, taking increasingly long in higher dimensions. In addition, hyper-rectangles tend to need a higher number of detectors to achieve adequate coverage of the solution space, though they exhibit very little overlapping among detectors. However, hyper-rectangles are consistently and considerably quicker to calculate detection for than the other shapes, which may make them a promising candidate for online detection schemes