You are previewing International Journal of Applied Evolutionary Computation (IJAEC) Volume 4, Issue 3.
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International Journal of Applied Evolutionary Computation (IJAEC) Volume 4, Issue 3

Book Description

The International Journal of Applied Evolutionary Computation (IJAEC) covers state-of-the-art interdisciplinary research on emerging areas and of intelligent computation (IC). By providing an academic and scientific forum for exchanging high quality results on innovative topics, trends and research in the field of IC, this journal expands the fields and the depths of its most principal and critical concepts. IJAEC extends existing research findings (theoretical innovations and modeling applications) to provide the highest quality original concepts, hybrid applications, innovative methodologies, and the development trends studies for all audiences. IJAEC publishes three categories of papers: research papers, research notes, and research review. Research papers have significant original research findings. The research must be complete and contribute substantially to knowledge in the field. Research notes comprise research that is complete but not as comprehensive as to meet the criteria of a full research paper. Research reviews are novel, insightful, and carefully crafted articles that conceptualize research areas and synthesize prior research. Research review articles must provide new insights that advance our understanding of the research areas and help in identifying and developing future research directions.

This issue contains the following articles:

  • Ant Colony Algorithms for Data Learning
  • Multimodal Approach for Emotion Recognition Using a Formal Computational Model
  • Clinical Practice Guidelines Formalization for Personalized Medicine
  • FMAMS: Fuzzy Mapping Approach for Mediation Systems
  • Simulation of a New Self-Structured Fuzzy Controller Applied to a Temperature Control Process
  • Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification
  • Hybridization of Artificial Neural Network and Particle Swarm Optimization Methods for Time Series Forecasting

Table of Contents

  1. Cover
  2. Masthead
  3. Call For Articles
  4. Special Issue in Memory of the Late Professor Lorenzo Ferrer Figueras
  5. Ant Colony Algorithms for Data Learning
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. FUZZY ANT-MINER ALGORITHM
    4. 3. CLUSTERING HETEROGENEOUS DATA BY ANT COLONY ALGORITHMS
    5. 4. IMPROVEMENTS BY OUR METHOD
    6. 5. EXAMPLE ILLUSTRATES THE METHOD
    7. 4. CONCLUSION
    8. REFERENCES
  6. Multimodal Approach for Emotion Recognition Using a Formal Computational Model
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. RELATED WORK
    4. 3. EMOTION RECOGNITION
    5. 4. EXPERIMENTS AND RESULTS
    6. 5. CONCLUSION
    7. ACKNOWLEDGMENT
    8. REFERENCES
  7. Clinical Practice Guidelines Formalization for Personalized Medicine
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. BACKGROUND
    4. 3. METHODS
    5. 4. RESULTS
    6. 5. DISCUSSION
    7. 6. CONCLUSION
    8. REFERENCES
  8. FMAMS:
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. APPLICATION DOMAINS
    4. 3. CLASSICAL MAPPING
    5. 4. MAPPING TYPES
    6. 5. PROBLEMATIC
    7. 6. OUR CONTRIBUTION: A FUZZY MAPPING FOR MEDIATION SYSTEMS
    8. 7. IMPLIMENTATION
    9. 8. CONCLUSION AND FUTURE WORKS
    10. REFERENCES
  9. Simulation of a New Self-Structured Fuzzy Controller Applied to a Temperature Control Process
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. DESIGN OF CONTROLLERS
    4. 3. DESCRIPTION OF THE HARDWARE MODEL
    5. 4. SIMULATION RESULTS
    6. 5. CONCLUSION
    7. REFERENCES
  10. Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification
    1. ABSTRACT
    2. INTRODUCTION
    3. BOOLEAN FUNCTION CLASSIFICATION
    4. LEARNING ALGORITHMS OF ARTIFICIAL NEURAL NETWORKS
    5. EVOLUTIONARY ALGORITHMS
    6. SIMULATION RESULTS AND DISCUSSION
    7. CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
  11. Hybridization of Artificial Neural Network and Particle Swarm Optimization Methods for Time Series Forecasting
    1. ABSTRACT
    2. 1. INTRODUCTION
    3. 2. TIME SERIES FORECASTING MODELS
    4. 3. PARTICLE SWARM OPTIMIZATION METHOD
    5. 4. THE PROPOSED HYBRID MECHANISM
    6. 5. EMPIRICAL RESULTS
    7. 6. CONCLUSION
    8. ACKNOWLEDGMENT
    9. REFERENCES
  12. Call For Articles