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

  • Saber Mohamed, "A new genetic algorithm for solving optimization problems", ELSEVIER, 2013 More
  • Saber Mohamed, "Particle Swarm Optimizer for Constrained Optimization", IEEE, 2013 More
  • Saber Mohamed, "Memetic Multi-Topology Particle Swarm Optimizer for Constrained Optimization", IEEE, 2012 More
  • Asmaa Atef Hassan El Sayed, "Composite Heuristic Priority Rules –Based on Tie-Breakers for Scheduling Multiple-Constrained Resource Projects", International Journal of Computer Applications Technology and Research (IJCATR), 2015 More
  • Mohammed Abdel Basset Metwally Attia, "A Simplex Method-Based Social Spider Optimization Algorithm for Clustering Analysis", Elsevier, 2017 More
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