@article{MAKHILLRJAS2013879320,
    title = {Prey-Predator Algorithm as a New Optimization Technique Using in Radial Basis Function Neural Networks},
    journal = {Research Journal of Applied Sciences},
    volume = {8},
    number = {7},
    pages = {383-387},
    year = {2013},
    issn = {1815-932x},
    doi = {rjasci.2013.383.387},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2013.383.387},
    author = {Nawaf Hamadneh Surafel,Saratha Sathasivam and},
    keywords = {Radial basis function neural network,Prey-Predator algorithm,Genetic algorithm,Metaheuristic,algorithm,optimization},
    abstract = {Prey-Predator algorithm is a new metaheuristic algorithm developed 
  for optimization problems. It is inspired by the interaction between a predator 
  and preys of animals in the ecosystem. In this study, researchers have used 
  the Prey-Predator algorithm to train the radial basis function neural networks. 
  The most important in the training is finding the parameters including the centers, 
  the widths and the output weights. Researchers have compared the performance 
  of the new algorithm with the genetic algorithm on a logic programming data, 
  iris flowers data set and new thyroid data set. The sum square error function 
  was used to evaluate the performance of the algorithms. From the computational 
  results, researchers found that Prey-Predator algorithm is better in improving 
  the performance of radial basis function neural.}
    }