1 GAs: What Are They?.- 2 GAs: How Do They Work?.- 3 GAs: Why Do They Work?.- 4 GAs: Selected Topics.- 5 Binary or Float?.- 6 Fine Local Tuning.- 7 Handling Constraints.- 8 Evolution Strategies and Other Methods.- 9 The Transportation Problem.- 10 The Traveling Salesman Problem.- 11 Evolution Programs for Various Discrete Problems.- 12 Machine Learning.- 13 Evolutionary Programming and Genetic Programming.- 14 A Hierarchy of Evolution Programs.- 15 Evolution Programs and Heuristics.- 16 Conclusions.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- References.

background

The Selfish Gene

Genetic Algorithms + Data Structures = Evolution Programs

Stakeholder theory has been a popular heuristic for describing the management environment for years, but it has not attained full theoretical status. Our aim in this article is to contribute to a theory of stakeholder identification and salience based on stakeholders possessing one or more of three relationship attributes: power, legitimacy, and urgency. By combining these attributes, we generate a typology of stakeholders, propositions concerning their salience to managers of the firm, and research and management implications.

https://www.jstor.org/stable/pdfplus/259247.pdf

Toward a Theory of Stakeholder Identification and Salience: Defining the Principle of who and What Really Counts

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.

/pdf/evolutionary-algorithms-for-solving-multi-objective-problems-2txxk4mqci.pdf

Evolutionary algorithms for solving multi-objective problems

Attachment--and loss.

This paper examines the progressive development of the new institutional economics over the past quarter century. It begins by distinguishing four levels of social analysis, with special emphasis on the institutional environment and the institutions of governance. It then turns to some of the good ideas out of which the NIE works: the description of human actors, feasibility, firms as governance structures, and operationalization. Applications, including privatization, are briefly discussed. Its empirical successes, public policy applications, and other accomplishments notwithstanding, there is a vast amount of unfinished business.

The New Institutional Economics: Taking Stock, Looking Ahead

Natural, physical, or material world and its phenomena

The Selfish Gene

Citations

Genetic Algorithms + Data Structures = Evolution Programs

Toward a Theory of Stakeholder Identification and Salience: Defining the Principle of who and What Really Counts

Evolutionary algorithms for solving multi-objective problems

Attachment--and loss.

The New Institutional Economics: Taking Stock, Looking Ahead

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