Training and Testing a Self-Adaptive Multi-Operator Evolutionary Algorithm for Constrained Optimization

Faculty Computer Science Year: 2015
Type of Publication: ZU Hosted Pages:
Authors:
Saber Mohamed
Journal: Applied Soft Computing ELSEVEIR Volume:
Keywords : Training , Testing , Self-Adaptive Multi-Operator Evolutionary Algorithm    
Abstract:
Over the last two decades, many different evolutionary algorithms (EAs) have been introduced for solving constrained optimization problems (COPs). Due to the variability of the characteristics in different COPs, no single algorithm performs
    
     
 
       

Author Related Publications

  • Saber Mohamed, "An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems", IEEE, 2013 More
  • Saber Mohamed, "Differential Evolution with Dynamic Parameters Selection for Optimization Problems", IEEE, 2014 More
  • Saber Mohamed, "A Self-Adaptive Combined Strategies Algorithm for Constrained Optimization using Differential Evolution", ELSEVIER, 2014 More
  • Saber Mohamed, "Self-adaptive Mix of Particle Swarm Methodologies for Constrained Optimization", ELSEVIER, 2014 More
  • Saber Mohamed, "Adaptive Configuration of Evolutionary Algorithms for Constrained Optimization", ELSEVIER, 2013 More

Department Related Publications

  • Karam mohamed goda, "Hybrid Gene Selection Methods of Microarray Data for Cancer Classification", International Journal of Graphics & Image Processing |Vol 2|issue 3|August 2012 , 2013 More
  • Saber Mohamed, "Evolving the Parameters of Differential Evolution using Evolutionary Algorithms", Springer, 2014 More
  • Saber Mohamed, "A Comparative Study of Different Variants of Genetic Algorithms for Constrained Optimization", Springer, 2010 More
  • Saber Mohamed, "Differential Evolution with Multiple Strategies for Solving CEC2011 Real-world Numerical Optimization Problems", IEEE, 2011 More
  • Eman samir hasan sayed, "Dependency Identification technique for large scale optimization problems", IEEE, 2012 More
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