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

  • Mohammed Abdel Basset Metwally Attia, "Discrete greedy flower pollination algorithm for spherical traveling salesman problem", Springer, 2019 More
  • Mohammed Abdel Basset Metwally Attia, "A New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems", Natural Sciences Publishing Cor., 2014 More
  • 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
Tweet