Agent-based computing / Duarte Bouca and Amaro Gafagnao, editors.
Gespeichert in:
| Beteiligte Personen | , |
|---|---|
| Ort, Verlag, Jahr |
New York
: Nova Science Publishers
, 2010
|
| Umfang | 1 online resource (348 p.) |
| ISBN | 1-61122-576-0 |
| Sprache | Englisch |
| Zusatzinfo | ""AGENT-BASED COMPUTING ""; ""AGENT-BASED COMPUTING ""; ""CONTENTS ""; ""PREFACE ""; ""AGENT-BASED GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION AND FEATURE SELECTION ""; ""1. INTRODUCTION ""; ""2. CHAIN-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION AND FEATURE SELECTION ""; ""2.1. Analysis of Algorithm ""; ""2.1.1. Chain-Like Agent Structure ""; ""2.1.2. Selection Process Based on Dynamic Neighboring Competition Strategy ""; ""2.1.3. Neighboring Crossover Process ""; ""2.1.4. Adaptive Mutation Process ""; ""2.1.5. Stop Criterion ""; ""2.1.6. Elitism Strategy"" ""2.1.7. Realization of Algorithm """"2.2. Experimental Results ""; ""2.2.1. Global Numerical Optimization Experiments ""; ""2.2.2. Feature Selection Experiments""; ""2.3. Conclusions ""; ""3. MULTIPLE-POPULATION CHAIN-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION AND FEATURE SELECTION ""; ""3.1. Analysis of Algorithm ""; ""3.1.1. Multi-Population Cycle Chain-Like Agent Structure ""; ""3.1.2. Genetic Operators""; ""3.1.3. Realization of Algorithm ""; ""3.1.4. Computational Complexity""; ""3.2. Experimental Results ""; ""3.2.1. Global Numerical Optimization Experiments "" ""3.2.2. Feature Selection Experiments""""3.3. Conclusions ""; ""CONCLUSIONS AND FUTURE WORK ""; ""ACKNOWLEDGMENTS""; ""REFERENCES ""; ""MULTI-AGENT ENTERPRISE SUSTAINABILITY PERFORMANCE MEASUREMENT SYSTEM""; ""ABSTRACT ""; ""INTRODUCTION ""; ""METHODOLOGY""; ""SUSTAINABILITY AGENT ""; ""1. The Selection of Suitable Indicators ""; ""2. Retrieving data from Data Repository Agent ""; ""3. Calculating the Weights of Indicators ""; ""4. Calculating Sustainability Performance Indices by Using MCDM Methods""; ""DATA REPOSITORY AGENT ""; ""ALERT MANAGEMENT AGENT ""; ""COMMUNICATION AGENT "" ""APPLICATION """"Sustainability Agent ""; ""Selecting the Proper Indicators ""; ""Retrieving the Data with Respect to the Indicators ""; ""Calculating the Importance Weights ""; ""Calculating the Performance Indices ""; ""Aggregate Ranking Using Copeland method ""; ""Calculating the Composite Sustainability Ranking Using Copeland method ""; ""ALERT MANAGEMENT AGENT ""; ""Communication Agent ""; ""DISCUSSION AND IMPLICATIONS ""; ""CONCLUSION ""; ""APPENDIX""; ""REFERENCES ""; ""A MODULAR ARTIFICIAL NEURAL NETWORK BASED DECISION MAKING IN A MULTI-AGENT ROBOT SOCCER SYSTEMS ""; ""ABSTRACT "" ""1. INTRODUCTION """"2. THE PROBLEM DESCRIPTION ""; ""3. THE BASIC ANN ARCHITECTURE ""; ""4. MODULAR ANN ARCHITECTURE ""; ""5. RESULTS AND DISCUSSION ""; ""CONCLUSION ""; ""REFERENCES""; ""SECURITY AND PRIVACY IN TRACK AND TRACE INFRASTRUCTURES ""; ""ABSTRACT ""; ""1. INTRODUCTION ""; ""1.1. Radio Frequency Identification ""; ""1.2. Track and Trace Infrastructures ""; ""2. SECURITY REQUIREMENTS ""; ""2.1. Confidentiality ""; ""2.2. Integrity ""; ""3. BATCH RECALLS ""; ""3.1. Example ""; ""3.2. Building Blocks ""; ""3.2.1. Identity-based Encryption ""; ""3.2.2. Boneh-Franklin Encryption "" ""3.2.3. Boneh-Boyen-Goh Encryption "" |
| Zusatzinfo | English |
| Serie/Reihe | Computer Science, Technology and Applications |
| Online-Zugang | EBSCO EBS 2024 |
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