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.

Consensus of opinion

For many societal decisions, governments and public bodies are beginning to involve stakeholders and the general public to a far greater extent than previously in the decision process. Stakeholder workshops, citizen juries, focus groups, electronic forums, web-polling and many other means of consultation are being used to draw citizens into the process of deciding between different options on the management of their communities. Politicians are drawn to such instruments because greater public involvement seems to achieve greater acceptance of the ultimate decision and, arguably in more objective terms, a better decision. Many academic studies have investigated participation and wider aspects of deliberative democracy and found that the politicians’ intuition is borne out in practice. However, while there have been many studies focused on specific instruments of participation, few have compared different ones. Moreover, there seems to be a dearth of advice on how to assemble a set of different instruments into a complete participatory decision making process. This paper offers a decision modelling framework which, firstly, provides a methodology which may be used to design participatory processes and, secondly, raises a number of questions which future comparative studies will need to address.

Designing a Participatory Process for Stakeholder Involvement in a Societal Decision

Retrospective studies of nuclear accidents such as those at Three Mile Island and Chernobyl have emphasized the need for preparing structured decision support methodologies for use in any future emergency. This paper discusses the multi-attribute aspects of such decision support, i.e. methods for supporting decisions when there are several conflicting objectives. Considerable progress has been made in application of these methods over the past 4 or 5 years. In particular, the decision-conferencing format has proved successful in helping senior decision makers understand and deal with the issues that arise in considering long-term countermeasures made. Within the RODOS project, a European initiative to build a decision support system for emergency response, multi-attribute value and utility techniques for short-term and medium-term countermeasures are being developed. However, much remains to be done, particularly in relation to the incorporation of uncertainty when there is a risk of an imminent accident.

Multi‐attribute decision support in the event of a nuclear accident

You can hardly tell where the computer models finish and the dinosaurs begin.Laura Dern, on the film Jurassic ParkDuring the past quarter century there have been many developments in scientific models and computer codes to help predict the ongoing consequences in the aftermath of many types of emergency: e.g. storms and flooding, chemical and nuclear accident, epidemics such as SARS and terrorist attack. Some of these models relate to the immediate events and can help in managing the emergency; others predict longer term impacts and thus can help shape the strategy for the return to normality. But there are many pitfalls in the way of using these models effectively. Firstly, non-scientists and, sadly, many scientists believe in the models predictions too much. The inherent uncertainties in the models are underestimated; sometimes almost unacknowledged. This means that initial strategies may later need to be revised in ways that unsettle the public, losing their trust in the emergency management process. Secondly, the output from these models form an extremely valuable input to the decision making process; but only one such input. There is a need to draw on much tacit knowledge which by definition cannot reside in a decision support system. Most emergencies are events that have huge social and economic impacts alongside the health and environmental consequences. While we can model the latter passably well, we are not so good at modelling economic impacts and very poor at modelling social impacts. Our knowledge of them is tacit and they lie in the complex space of Snowdens Cynefin categorisation of decision contexts. Thus we draw upon recent thinking in both decision support and knowledge management systems to suggest that we need a more socio-technical approach to developing crisis response system; and, in particular, we explore how model predictions should be drawn into emergency management processes in more balanced ways than often has occurred in the past.

Believe in the Model: Mishandle the Emergency

Current guidance in the UK and elsewhere indicate upper and target risk limits for the operation of nuclear plant in terms of individual risk per annum. ‘As low as reasonably practicable’ (ALARP) arguments are used to justify the acceptance or rejection of policies that lead to risk changes between these limits. The suitability of cost‐benefit analysis (CBA) and multiattribute utility theory (MAUT) are assessed for performing ALARP (‘as low as reasonably possible’) assessments, in particular within the nuclear industry. Four problems stand out in current CBA applications to ALARP, concerning the determination of prices of safety gains or detriments, the valuation of group and individual risk, calculations using ‘disproportionality’, and the use of discounting to trade‐off risks through time. This last point has received less attention in the past but is important because of the growing interest in risk‐informed regulation in which policies extend over several timeframes and distribute the risk unevenly ov...

Simon French

Papers

Consensus of opinion

Designing a Participatory Process for Stakeholder Involvement in a Societal Decision

Multi‐attribute decision support in the event of a nuclear accident

Believe in the Model: Mishandle the Emergency

Supporting ALARP decision making by cost benefit analysis and multiattribute utility theory