Evolutionary algorithms (EAs) are well-established techniques to approach those problems which for the classical optimization methods are difficult to solve. Tackling problems with mixed-type of variables, many local optima, undifferentiable or non-analytical functions are some examples to highlight the outstanding capabilities of the evolutionary algorithms. Among the various kinds of evolutionary algorithms, differential evolution (DE) is well known for its effectiveness and robustness. Many comparative studies confirm that the DE outperforms many other optimizers. Finding more accurate solution(s), in a shorter period of time for complex black-box problems, is still the main goal of all evolutionary algorithms. The opposition concept, on the other hand, has a very old history in philosophy, set theory, politics, sociology, and physics. But, there has not been any opposition-based contribution to optimization. In this thesis, firstly, the opposition-based optimization (OBO) is constituted. Secondly, its advantages are formally supported by establishing mathematical proofs. Thirdly, the opposition-based acceleration schemes, including opposition-based population initialization and generation jumping, are proposed. Fourthly, DE is selected as a parent algorithm to verify the acceleration effects of proposed schemes. Finally, a comprehensive set of well-known complex benchmark functions is employed to experimentally compare and analyze the algorithms. Results confirm that opposition-based DE (ODE) performs better than its parent (DE), in terms of both convergence speed and solution quality. The main claim of this thesis is not defeating DE, its numerous versions, or other optimizers, but to introduce a new notion into nonlinear continuous optimization via innovative metaheuristics, namely the notion of opposition. Although, ODE has been compared with six other optimizers and outperforms them overall. Furthermore, both presented experimental and mathematical results conform with each other and demonstrate that opposite points are more beneficial than pure random points for black-box problems; this fundamental knowledge can serve to accelerate other machine learning approaches as well (such as reinforcement learning and neural networks). And perhaps in future, it could replace the pure randomness with random-opposition model when there is no a priori knowledge about the solution/problem. Although, all conducted experiments utilize DE as a parent algorithm, the proposed schemes are defined at the population level and, hence, have an inherent potential to be utilized for acceleration of other DE extensions or even other population-based algorithms, such as genetic algorithms (GAs). Like many other newly introduced concepts, ODE and the proposed opposition-based schemes still require further studies to fully unravel their benefits, weaknesses, and limitations.

Machine intelligence techniques contribute to solving real-world problems. Reinforcement learning (RL) is one of the machine intelligence techniques with several characteristics that make it suitable for the applications, for which the model of the environment is not available to the agent. In real-world applications, intelligent agents generally face a very large state space which limits the usability of reinforcement learning. The condition for convergence of reinforcement learning implies that each state-action pair must be visited infinite times, a condition which can be considered impossible to be satisfied in many practical situations. The goal of this work is to propose a class of new techniques to overcome this problem for off-policy, step-by-step (incremental) and model-free reinforcement learning with discrete state and action space. The focus of this research is using the design characteristics of RL agent to improve its performance regarding the running time while maintaining an acceptable level of accuracy. One way of improving the performance of the intelligent agents is using the model of environment. In this work, a special type of knowledge about the agent actions is employed to improve its performance because in many applications the model of environment may only be known partially or not at all. The concept of opposition is employed in the framework of reinforcement learning to achieve this goal. One of the components of RL agent is the action. For each action we define its associate opposite action. The actions and opposite actions are implemented in the framework of reinforcement learning to update the value function resulting in a faster convergence. At the beginning of this research the concept of opposition is incorporated in the components of reinforcement learning, states, actions, and reinforcement signal which results in introduction of the oppositional target domain estimation algorithm, OTE. OTE reduces the search and navigation area and accelerates the speed of search for a target. The OTE algorithm is limited to the applications, in which the model of the environment is provided for the agent. Hence, further investigation is conducted to extend the concept of opposition to the model-free reinforcement learning algorithms. This extension contributes to the generating of several algorithms based on using the concept of opposition for Q(lambda) technique. The design of reinforcement learning agent depends on the application. The emphasize of this research is on the characteristics of the actions. Hence, the primary challenge of this work is design and incorporation of the opposite actions in the framework of RL agents. In this research, three different applications, namely grid navigation, elevator control problem, and image thresholding are implemented to address this challenge in context of different applications. The design challenges and some solutions to overcome the problems and improve the algorithms are also investigated. The opposition-based Q(lambda) algorithms are tested for the applications mentioned earlier. The general idea behind the opposition-based Q(lambda) algorithms is that in Q-value updating, the agent updates the value of an action in a given state. Hence, if the agent knows the value of opposite action then instead of one value, the agent can update two Q-values at the same time without taking its corresponding opposite action causing an explicit transition to opposite state. If the agent knows both values of action and its opposite action for a given state, then it can update two Q-values. This accelerates the learning process in general and the exploration phase in particular. Several algorithms are outlined in this work. The OQ(lambda) will be introduced to accelerate Q(lambda) algorithm in discrete state spaces. The NOQ(lambda) method is an extension of OQ(lambda) to operate in a broader range of non-deterministic environments. The update of the opposition trace in OQ(lambda) depends on the next state of the opposite action (which generally is not taken by the agent). This limits the usability of this technique to the deterministic environments because the next state should be known to the agent. NOQ(lambda) will be presented to update the opposition trace independent of knowing the next state for the opposite action. The results show the improvement of the performance in terms of running time for the proposed algorithms comparing to the standard Q(lambda) technique.

Evolutionary algorithms (EAs) are well-established techniques to approach those problems which for the classical optimization methods are difficult to solve. Tackling problems with mixed-type of variables, many local optima, undifferentiable or non-analytical functions are some examples to highlight the outstanding capabilities of the evolutionary algorithms. Among the various kinds of evolutionary algorithms, differential evolution (DE) is well known for its effectiveness and robustness. Many comparative studies confirm that the DE outperforms many other optimizers. Finding more accurate solution(s), in a shorter period of time for complex black-box problems, is still the main goal of all evolutionary algorithms. The opposition concept, on the other hand, has a very old history in philosophy, set theory, politics, sociology, and physics. But, there has not been any opposition-based contribution to optimization. In this thesis, firstly, the opposition-based optimization (OBO) is constituted. Secondly, its advantages are formally supported by establishing mathematical proofs. Thirdly, the opposition-based acceleration schemes, including opposition-based population initialization and generation jumping, are proposed. Fourthly, DE is selected as a parent algorithm to verify the acceleration effects of proposed schemes. Finally, a comprehensive set of well-known complex benchmark functions is employed to experimentally compare and analyze the algorithms. Results confirm that opposition-based DE (ODE) performs better than its parent (DE), in terms of both convergence speed and solution quality. The main claim of this thesis is not defeating DE, its numerous versions, or other optimizers, but to introduce a new notion into nonlinear continuous optimization via innovative metaheuristics, namely the notion of opposition. Although, ODE has been compared with six other optimizers and outperforms them overall. Furthermore, both presented experimental and mathematical results conform with each other and demonstrate that opposite points are more beneficial than pure random points for black-box problems; this fundamental knowledge can serve to accelerate other machine learning approaches as well (such as reinforcement learning and neural networks). And perhaps in future, it could replace the pure randomness with random-opposition model when there is no a priori knowledge about the solution/problem. Although, all conducted experiments utilize DE as a parent algorithm, the proposed schemes are defined at the population level and, hence, have an inherent potential to be utilized for acceleration of other DE extensions or even other population-based algorithms, such as genetic algorithms (GAs). Like many other newly introduced concepts, ODE and the proposed opposition-based schemes still require further studies to fully unravel their benefits, weaknesses, and limitations.

Machine intelligence techniques contribute to solving real-world problems. Reinforcement learning (RL) is one of the machine intelligence techniques with several characteristics that make it suitable for the applications, for which the model of the environment is not available to the agent. In real-world applications, intelligent agents generally face a very large state space which limits the usability of reinforcement learning. The condition for convergence of reinforcement learning implies that each state-action pair must be visited infinite times, a condition which can be considered impossible to be satisfied in many practical situations. The goal of this work is to propose a class of new techniques to overcome this problem for off-policy, step-by-step (incremental) and model-free reinforcement learning with discrete state and action space. The focus of this research is using the design characteristics of RL agent to improve its performance regarding the running time while maintaining an acceptable level of accuracy. One way of improving the performance of the intelligent agents is using the model of environment. In this work, a special type of knowledge about the agent actions is employed to improve its performance because in many applications the model of environment may only be known partially or not at all. The concept of opposition is employed in the framework of reinforcement learning to achieve this goal. One of the components of RL agent is the action. For each action we define its associate opposite action. The actions and opposite actions are implemented in the framework of reinforcement learning to update the value function resulting in a faster convergence. At the beginning of this research the concept of opposition is incorporated in the components of reinforcement learning, states, actions, and reinforcement signal which results in introduction of the oppositional target domain estimation algorithm, OTE. OTE reduces the search and navigation area and accelerates the speed of search for a target. The OTE algorithm is limited to the applications, in which the model of the environment is provided for the agent. Hence, further investigation is conducted to extend the concept of opposition to the model-free reinforcement learning algorithms. This extension contributes to the generating of several algorithms based on using the concept of opposition for Q(lambda) technique. The design of reinforcement learning agent depends on the application. The emphasize of this research is on the characteristics of the actions. Hence, the primary challenge of this work is design and incorporation of the opposite actions in the framework of RL agents. In this research, three different applications, namely grid navigation, elevator control problem, and image thresholding are implemented to address this challenge in context of different applications. The design challenges and some solutions to overcome the problems and improve the algorithms are also investigated. The opposition-based Q(lambda) algorithms are tested for the applications mentioned earlier. The general idea behind the opposition-based Q(lambda) algorithms is that in Q-value updating, the agent updates the value of an action in a given state. Hence, if the agent knows the value of opposite action then instead of one value, the agent can update two Q-values at the same time without taking its corresponding opposite action causing an explicit transition to opposite state. If the agent knows both values of action and its opposite action for a given state, then it can update two Q-values. This accelerates the learning process in general and the exploration phase in particular. Several algorithms are outlined in this work. The OQ(lambda) will be introduced to accelerate Q(lambda) algorithm in discrete state spaces. The NOQ(lambda) method is an extension of OQ(lambda) to operate in a broader range of non-deterministic environments. The update of the opposition trace in OQ(lambda) depends on the next state of the opposite action (which generally is not taken by the agent). This limits the usability of this technique to the deterministic environments because the next state should be known to the agent. NOQ(lambda) will be presented to update the opposition trace independent of knowing the next state for the opposite action. The results show the improvement of the performance in terms of running time for the proposed algorithms comparing to the standard Q(lambda) technique.

The essay is dealing with the different tensions in hiphop created when appearing in Swedish media. With the teoretical aspect of "the Other" as a tool, the authors of the essay has tried to find out how the "hiphopper" is pictured in swedish written media within the timeframe of 19??-??. We are also discussin the different aspects and importance of autenticity, etnitcity and geografy in regards to the "hiphoper" as "the Other".

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With the coming era of Internet and knowledge economy, the importance of IS/IT(information systems/information technology) to enterprises can not be overemphasized. However, if IS/IT couldn¡¦t be implemented successfully and brings its performance into full play, the effectiveness and usefulness of IS/IT are unmeaningful. The implementation of IS/IT in an organization is a complicated and dynamic process, which is effected by a variety of factors. For instance, technical factors, the psychological and behavior factors of users, even the cultural, political, economic, and institutional factors rest on the level of organization or environment may all cause the critical effects of implementing IS/IT. For the purpose of understanding the whole picture of IS/IT implementation process, this study integrates the perspective of contradiction/opposition into Structuration Theory and then proposes an interpretive framework, which can contribute to the interpretation of how and why the organization changes and develops. The framework can be used to identify the mechanism behind changing events and the context connected with them, therefore, we can adequately make sense of the complicated and dynamic social process of IS/IT implementation. By distinguishing the contradiction/opposition between structures of agents, between social structures, and between agent structure and social structure, the study interprets how and why agents respond to the enabling/constraint forces caused by structures. According to this, we can understand the unique structuration of IS/IT implementation process in an organization. For the application of the integrated framework, this study adopts qualitative research methodology and the framework for guidance to study an intensive case of ERP implementation. Through the data collection and analysis, this study identifies all of the contradiction/opposition between social entities, and the enabling/constraint social forces during the process of ERP implementation. The result reveals that the severe contradictions/oppositions between social entities caused the failure of the ERP project in Phase I. Entering the Phase II, in addition to the transformation of some structures of social entities, the new forming MIS structure exercised its political and expert power to make ERP system finally perform well. In Phase III, the case company went back to the previous stable status, because the ERP project ended and main project members withdrew. At the end of the paper, this study proposes some conclusions and suggestions for practitioners and academia, and also shows the limitation of the study.

This thesis analyses the Muslim Brotherhood&rsquo / s fluctuated relationship with three of the Egyptian governments for the post-Revolutionary period. It is argued that the Muslim Brotherhood and the Egyptian Governments were firstly accommodated each other during the legitimacy processes of the governments. However, after the Muslim Brotherhood began to use the governments&rsquo / concessions to infiltrate the social and political field the Movement began to be seen as a threat by the governments and the relationship between the sides transformed to confrontation. At that point the turning points in the accommodation and confrontation relationship and particularly the political strategies of the both sides to protect their influences were analyzed by taking into account the domestic issues of Egypt, internal issues of the Muslim Brotherhood and international atmosphere.

Dissertation--Bonn--Universität, 2002. / SED = Sozialistische Einheitspartei Deutschlands, PDS = Partei des demokratischen Sozialismus. Bibliogr. p. 293-321.

The development of affordable housing often involves a contentious siting process. Proposed housing developments frequently trigger concern among neighbors and community groups about potential negative impacts on neighborhood quality of life and property values. Advocates, developers, and researchers have long suspected that some of these concerns stem from racial or class prejudice, yet, to date, these assumptions lack empirical evidence. My research seeks to examine the roles that perceptions of race and class play in shaping opinions that underlie public opposition to affordable housing. Such opposition often earns the label "Not in my Backyard" (NIMBY). The application of a mixed-methods approach helps determine why the public opposes the development of affordable housing in their neighborhoods and towns. The focus group and survey results provide a rich understanding of the underlying attitudes that trigger opposition to affordable housing when proposed nearby. This study demonstrates that stereotypes and perceptions of the poor and minorities are particularly strong determinants of affordable housing opposition. This research improves our understanding of public attitudes toward affordable housing attitudes, leading to a more focused and effective policies and plans for the siting of affordable housing. The results provide advocates, planners, developers, and researchers with a more accurate portrayal of affordable housing opposition, thereby allowing the response to be shaped in a more appropriate manner. / text