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.

The success of e-democracy, large-scale knowledge creation, and social networking activities depends on wide participation and effective collaboration. Successful participation and collaboration needs effective facilitation support that can engage people, coordinate their joint actions and advance their common understanding. This work addresses the emerging facilitation support issues by developing a multi-dimensional framework for wide participation and common understanding. The dimensions of the framework include methodology, technology, community, social cognitive interaction, participation space, and culture type. Such a framework can help us characterise the issues, describe the settings, understand, infer and explain the kind of facilitation support we may need, so as to guide the design and use of Web based collaborative systems.

A multi-dimensional framework for facilitating wide participation and common understanding

The objective of the CONFIDENCE project has been to identify, assess and communicate the uncertainties that arise in managing and recovering from a nuclear accident. To do that, it is important to be clear on what uncertainty means, how it arises and how we might analyse it. In fact, there are many forms of uncertainty, some of which we can assess with current tools, but some of which are more nebulous and difficult to deal with. Nonetheless, all need to be communicated to the emergency managers. Starting with a review of different categories of uncertainties, and using examples from the CONFIDENCE project, this paper discusses how the various uncertainties are manifested in nuclear emergency management. It concludes with a simple framework for categorising and analysing them. The overall intention is that such a framework could contribute to a broader discussion on identifying and handling uncertainties with nuclear emergency management actors

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Different types of uncertainty in nuclear emergency management

Decision support systems for off-site nuclear emergency management must address the inherent uncertainty in an evolving and effectively unique accident sequence. From the earliest moments when an accidental release threatens, through any release, to the long term consequences arising from the resulting contamination, there are many uncertainties to be weighed in selecting countermeasure strategies. Many of these uncertainties can be reduced through the careful collection and analysis of variety of data. The ways in which data can and should be analysed to achieve this are discussed. It is argued that there is no place for ad hoc approaches in designing a complex decision support system to underpin emergency management. The analyses must fit together so that data and assessments of uncertainty can be passed from module to module in a seamless fashion.

Uncertainty modelling, data assimilation and decision support for management of off-site nuclear emergencies

One of the strengths of decision analysis is that it can deal with most uncertainties; but, alas, not all. Sometimes uncertainties are too deep: that is, within the time and data currently availabl...

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Axiomatizing the Bayesian Paradigm in Parallel Small Worlds

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis We comment on the somewhat limited literature on the representation of spatial uncertainty on maps We note that many cognitive issues arise and that the potential for confusion is high We note that in the early stages of handling a crisis, the uncertainties involved may be deep, ie, difficult or impossible to quantify in the time available In such circumstance, we suggest the idea of presenting multiple scenarios

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Simon French

Papers

A multi-dimensional framework for facilitating wide participation and common understanding

Different types of uncertainty in nuclear emergency management

Uncertainty modelling, data assimilation and decision support for management of off-site nuclear emergencies

Axiomatizing the Bayesian Paradigm in Parallel Small Worlds

Communicating Geographical Risks in Crisis Management: The Need for Research.