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The ill-effects of stress due to fatigue significantly impact the welfare of individuals and consequently impact overall corporate productivity. This study introduces a simplified model of stress in the workplace using agent-based simulation. This study represents a novel contribution to the field of evolutionary computation. Agents are encoded initially using a String Representation and later expanded to multi-state Binary Decision Automata to choose between work on a base task, special project or rest. Training occurs by agents inaccurately mimicking behaviour of highly productive mentors. Stress is accumulated through working long hours thereby decreasing productivity performance of an agent. Lowest productivity agents are fired or retrained. The String representation for agents demonstrated near average performance attributed to the normally distributed tasks assigned to the string. The BDA representation was found to be highly adaptive, responding robustly to parameter changes. By reducing the number of simplifications for the model, a more accurate representation of the real world can be achieved.

The focus of this research is to provide an efficient approach to deal with computationally expensive Multiobjective Optimization Problems (MOP’s). Typically, approximation or surrogate based techniques are adopted to reduce the computational cost. In such cases, the original expensive objective function is replaced by a cheaper mathematical model, where this model mimics the behavior/input-output (i.e. design variable – objective value) relationship. However, it is difficult to model an exact substitute of the targeted objective function. Furthermore, if this kind of approach is used in an evolutionary search, literally, the number of function evaluations does not reduce (i.e. The number of original function evaluation is replaced by the number of surrogate/approximate function evaluation). However, if a large number of individuals are considered, the surrogate model fails to offer smaller computational cost. / To tackle this problem, we have reformulated the concept of surrogate modeling in a different way, which is more suitable for the Multiobjective Evolutionary Algorithm(MOEA) paradigm. In our approach, we do not approximate the objective function; rather we model the input-output behavior of the underlying MOEA itself. The model attempts to identify the search path (in both design-variable and objective spaces) and from this trajectory the model is guaranteed to generate non-dominated solutions (especially, during the initial iterations of the underlying MOEA – with respect to the current solutions) for the next iterations of the MOEA. Therefore, the MOEA can avoid re-evaluating the dominated solutions and thus can save large amount of computational cost due to expensive function evaluations. We have designed our approximation model as a variation operator – that follows the trajectory of the fronts and can be “plugged-in” to any kind of MOEA where non-domination based selection is used. Hence it is termed– the “Pareto-Following Variation Operator (PFVO)”. This approach also provides some added advantage that we can still use the original objective function and thus the search procedure becomes robust and suitable, especially for dynamic problems. / We have integrated the model into three base-line MOEA’s: “Non-dominated Sorting Genetic Algorithm - II (NSGA-II)”, “Strength Pareto Evolutionary Algorithm - II (SPEAII)”and the recently proposed “Regularity Model Based Estimation of Distribution Algorithm (RM-MEDA)”. We have also conducted an exhaustive simulation study using several benchmark MOP’s. Detailed performance and statistical analysis reveals promising results. As an extension, we have implemented our idea for dynamic MOP’s. We have also integrated PFVO into diffusion based/cellular MOEA in a distributed/Grid environment. Most experimental results and analysis reveal that PFVO can be used as a performance enhancement tool for any kind of MOEA.

Thesis (M.S.)--Missouri University of Science and Technology, 2008. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 29, 2008) Includes bibliographical references (p. 53-55).

Thesis (M.S.)--Missouri University of Science and Technology, 2009. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed August 10, 2009) Includes bibliographical references (p. 49-51).

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学位授与大学：京都大学 ; 取得学位: 博士(工学) ; 学位授与年月日: 2007-09-25 ; 学位の種類: 新制・課程博士 ; 学位記番号: 工博第2855号 ; 請求記号: 新制/工/1420 ; 整理番号: 25540 / Design problem solving behavior refers to the way in which people solve their creative problem of design in their mind. It is one of the basic problems in the area of design methodology, which varies greatly by cases and designers. On the other hand, there are still some general ways or commonness as the core. Because of the complexity of design problem solving behavior, it is still not understood very well. This dissertation dives into the problem of design problem solving behavior too and tried to provide a general view of it, including both the general strategies and the temporary tactics. But differs from many other researches, it employed a confined and well-structured simulation of manual design process by employing the method of interactive evolutionary computation (IEC) to extract design problem solving behavior objectively. The simulated design process provided a comparable and statistically analyzable model for exploring design problem solving behavior of people, and made the findings of this dissertation more reliable. The design problem of interior works of Chinese residents, which need little special knowledge to solve, was selected as the design problem in this dissertation. The method of IEC was applied in interior works design for helping the Chinese residents to solve the practical interior works design problems, and inducing the design problem solving behavior of them. The dissertation contains 6 chapters, including the general introduction (chapter 1), the main body (chapter 2 to 5), and the conclusion (chapter 6). The main body can be further divided into two parts. In the first part (chapter 2 and 3) the IEC interior works (IECIW) design system was developed, and evaluated by a large amount of Chinese residents on its usability and disadvantage. After the preparation of method in the first part, the second part (chapter 4 and 5) presented two parallel researches on participants’ design problem solving behavior in design process using IEC in order to approach the design problem solving behavior in common design processes. Chapter 1 introduces the background and purpose of the research, reviewed related literatures, and the frame work of the dissertation. In chapter 2, IEC method was tentatively applied in the problem of interior works design. 7 color and texture related factors of the living room of a typical apartment in Beijing were selected as design factors in the IEC IW design system. Through 3 experiments, the IEC IW design system was found effective in interior works design and heuristic for the two tested Chinese students. The effect of increasing population size was also found significantly increasing the efficiency of the system. In chapter 3, the developed IEC IW design system was tentatively used by 231 Chinese residents to evaluate its usability and disadvantage in real design problems of interior works. It was concluded that the IEC IW design system is useful for the residents, and it was also found that older participants, and those with lower education and family income levels, gave the system better evaluations. Chapter 4 started to explore problem solving behavior of people in design tasks through simulated design process for interior works using IEC. Data of design process employing IEC of 8 Chinese participants were collected. Through analysis of design problem solving process, it was revealed that people tend to do what they are certain of firstly, and make harder decisions later. It was also found that people did not tend to move their eyes to a faraway image in the interface constantly, which was considered more convenient for them. Chapter 5 continued to explore problem solving behavior of the 8 participants' interior works design process employing IEC. The method of protocol analysis was employed to analyze verbal reports of the participants. It was revealed that different parts of the interior scene have different influence on people's evaluation, and people tended to use same evaluation criterion continuously on several images, then switch to another evaluation criterion. 3 stages of design problem solving behavior along the process were also explained. Chapter 6 summarizes the findings in the dissertation, presents the general discussion and perspective, and proposed some research in the future. / Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第13384号 / 工博第2855号 / 新制||工||1420(附属図書館) / 25540 / UT51-2007-Q785 / 京都大学大学院工学研究科建築学専攻 / (主査)教授 宗本 順三, 教授 上谷 宏二, 教授 加藤 直樹 / 学位規則第4条第1項該当

The evolution of explicitly represented topologies such as graphs involves devising methods for mutating, comparing and combining structures in meaningful ways and identifying and maintaining the necessary topological diversity. Research has been conducted in the area of the evolution of trees in genetic programming and of neural networks and some of these problems have been addressed independently by the different research communities. In the domain of neural networks, NEAT (Neuroevolution of Augmenting Topologies) has shown to be a successful method for evolving increasingly complex networks. This system's success is based on three interrelated elements: speciation, marking of historical information in topologies, and initializing search in a small structures search space. This provides the dynamics necessary for the exploration of diverse solution spaces at once and a way to discriminate between different structures. Although different representations have emerged in the area of genetic programming, the study of the tree representation has remained of interest in great part because of its mapping to programming languages and also because of the observed phenomenon of unnecessary code growth or bloat which hinders performance. The structural similarity between trees and neural networks poses an interesting question: Is it possible to apply the techniques from NEAT to the evolution of trees and if so, how does it affect performance and the dynamics of code growth? In this work we address these questions and present analogous techniques to those in NEAT for genetic programming.

The main goal of a scheduling process is to decide when and how to execute each of the project’s activities. Despite large variety of researched scheduling problems, the majority of them can be described as generalisations of the resource-constrained project scheduling problem (RCPSP). Because of wide applicability and challenging difficulty, RCPSP has attracted vast amount of attention in the research community and great variety of heuristics have been adapted for solving it. Even though these heuristics are structurally different and operate according to diverse principles, they are designed to obtain only one solution at a time. In the recent researches on RCPSPs, it was proven that these kind of problems have complex multimodal fitness landscapes, which are characterised by a wide solution search spaces and presence of multiple local and global optima. The main goal of this thesis is twofold. Firstly, it presents a variation of the RCPSP that considers optimisation of projects in an uncertain environment where resources are modelled to adapt to their environment and, as the result of this, improve their efficiency. Secondly, modification of a novel evolutionary computation method Cuckoo Search (CS) is proposed, which has been adapted for solving combinatorial optimisation problems and modified to obtain multiple solutions. To test the proposed methodology, two sets of experiments are carried out. First, the developed algorithm is applied to a real-life software development project. Second, performance of the algorithm is tested on universal benchmark instances for scheduling problems which were modified to take into account specifics of the proposed optimisation model. The results of both experiments demonstrate that the proposed methodology achieves competitive level of performance and is capable of finding multiple global solutions, as well as prove its applicability in real-life projects.

yes / Shear failure of concrete elements reinforced with Fiber Reinforced Polymer (FRP) bars is generally brittle, requiring accurate predictions to avoid it. In the last decade, a variety of artificial intelligence based approaches have been successfully applied to predict the shear capacity of FRP Reinforced Concrete (FRP-RC). In this paper, a new approach, namely, biogeography-based programming (BBP) is introduced for predicting the shear capacity of FRP-RC beams based on test results available in the literature. The performance of the BBP model is compared with several shear design equations, two previously developed artificial intelligence models and experimental results. It was found that the proposed model provides the most accurate results in calculating the shear capacity of FRP-RC beams among the considered shear capacity models. The proposed BBP model can also correctly predict the trend of different influencing variables on the shear capacity of FRP-RC beams.