Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.

Numerical Optimization

List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

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Evolutionary algorithms for solving multi-objective problems

Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a h...

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Confirmation Bias: A Ubiquitous Phenomenon in Many Guises:

phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. It has several automation features and is also highly flexible. Several hundred parameters enable extensive customizations for complex use cases. Multiple user-defined refinement strategies can be applied to specific parts of the model in a single refinement run. An intuitive graphical user interface is available to guide novice users and to assist advanced users in managing refinement projects. X-ray or neutron diffraction data can be used separately or jointly in refinement. phenix.refine is tightly integrated into the PHENIX suite, where it serves as a critical component in automated model building, final structure refinement, structure validation and deposition to the wwPDB. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods.

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Towards automated crystallographic structure refinement with phenix.refine

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Few systems operate completely independent of humans. Thus any study of system risk or reliability requires analysis of the potential for failure arising from human activities in operating and managing this. Human reliability analysis (HRA) grew up in the 1960s with the intention of modelling the likelihood and consequences of human error. Initially, it treated the humans as any other component in the system. They could fail and the consequences of their failure were examined by tracing the effects through a fault tree. Thus to conduct a HRA one had to assess the probability of various operator errors, be they errors of omission or commission. First generation HRA may have used some sophistication in accomplishing this, but in essence that is all they did. Over the years, methods have been developed that recognise human potential to recover from a failure, on the one hand, and the effects of stress and organisational culture on the likelihood of possible errors, on the other. But no method has yet been developed which incorporates all our understanding of individual, team and organisational behaviour into overall assessments of system risk or reliability.

Human Reliability Analysis: A Review and Critique

Manchester Business School employs a distinctive approach to learning known as the Manchester Method which is based on the principle that the most effective and rewarding way to learn and remember is through a practical reflective, live/real project-based approach. This paper investigates the use of a collaboration and information sharing application, IBM Lotus QuickPlace, for enhancing the Manchester learning experience.

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Adopting a Web-based Collaborative Tool to Support the Manchester Method Approach to Learning

Intertemporal decision making involves decisions that have consequences that span several periods of time and often extend far into the future. The purpose of this paper is to discuss and highlight the differences associated with different evaluation methods designed to cope with the long-term impacts of a decision including discounting. The concepts and ideas are illustrated in the context of a decision about a nuclear waste facility. We show how applying different discounting methodologies can greatly affect the decision made, especially over long time periods. © 1998 John Wiley & Sons, Ltd.

Valuing the Future: a MADA example involving nuclear waste storage

The last 20 years has seen enormous advances in mathematical modelling and computing techniques. In the aftermath of the Chernobyl Accident, many of these have been incorporated in Decision Support Systems (DSS) to aid nuclear emergency management. This paper reviews what has been achieved; but it also reflects on how the tools fit into emergency management processes and discusses whether too much emphasis is being placed on the technological aspects of what is a complex, socio-technical issue.

Decision support in nuclear and radiological emergency situations: Are we too focused on models and technology?

We report on the development of the Bayesian forecasting and uncertainty handling components of a decision support for emergency management in the event of an accidental release of radioactivity. In particular, we focus on the forecasting of the spread of the contamination. We describe a simple but novel form of stochastic Bayes linear model. This closely mirrors well-developed (Gaussian) puff atmospheric dispersion models, but admits effective Bayesian learning procedures on the values of uncertain variables. Several features of the model are highlighted and its workings illustrated on (partially simulated) test data. In the event of an accidental release of radioactivity, the dispersal of the contaminated material will be of prime concern in determining appropriate countermeasures. A European consortium, funded by the Commission of European Communities (CEC), is currently developing a decision support system (DSS) to aid emergency management in such events. It is envisaged that this DSS will be used to guide both short-term counter- measures, such as sheltering and evacuation, and also longer-term countermeasures, such as food bans and relocation. At its heart needs to be an atmospheric dispersion model to provide a forecast of the spread of the plume. Currently, such models, despite their complexity, are essentially deterministic. They neither allow a simple means of assimilat- ing monitoring data nor provide realistic measures of the uncertainty in their forecasts. Our role in the consortium is to investigate the feasibility of using the Bayesian metho- dology to provide data assimilation and uncertainty management in the DSS. We have begun by focusing on atmospheric dispersion models. Subsequently, our efforts will be directed to models used to forecast the efficacy of countermeasures and the evaluation of different possible strategies. There are essentially four sources of uncertainty associated with forecasting atmospheric dispersion. * Data concerning actual emissions or the source term: If the release occurs in a 'controlled' way through the stack at a power plant, knowledge of the mass and spectrum of the released contaminants will be quite precise. On other hand, in the event of a Chernobyl-like accident, direct information on the emissions will be very sparse. * Errors inherent in the air and ground contamination readings: Monitoring observations taken to locate the spread of the plume are subject to variability both from observational error (counting statistics and human error) and from the natural heterogeneity of the contamination caused by local geographic features, wind behaviour and precipitation. * Uncertainty associated with the meteorological forecasts: Weather forecasting is an uncertain science. Also, dispersal other than over a flat terrain in stable weather conditions depends on local wind patterns, which in turn depend in complex ways on the local geography. * Modelling error inherent in the use of a particular atmospheric dispersion model: Such models, although extremely complex, will typically fail to incorporate enough contingencies to enable them to work well in all circumstances.

Simon French

Papers

Human Reliability Analysis: A Review and Critique

Adopting a Web-based Collaborative Tool to Support the Manchester Method Approach to Learning

Valuing the Future: a MADA example involving nuclear waste storage

Decision support in nuclear and radiological emergency situations: Are we too focused on models and technology?

Bayesian updating of atmospheric dispersion models for use after an accidental release of radioactivity