Difference between revisions of "Presentation"

From Adaptive Population based Simplex
Jump to: navigation, search
m (Change Expansion to Reflection)
 
(4 intermediate revisions by one other user not shown)
Line 4: Line 4:
 
rect 6 5 368 122 [[Initialisation|Initialisation]]
 
rect 6 5 368 122 [[Initialisation|Initialisation]]
 
rect 198 234 563 346 [[Selection|Selection]]
 
rect 198 234 563 346 [[Selection|Selection]]
rect 199 426 562 539 [[Reflection|Expansion]]
+
rect 199 426 562 539 [[Expansion|Expansion]]
 
rect 198 617 563 732 [[Contraction|Contraction]]
 
rect 198 617 563 732 [[Contraction|Contraction]]
 
rect 197 810 564 925 [[Local search|Local search]]
 
rect 197 810 564 925 [[Local search|Local search]]
Line 14: Line 14:
 
APS has been inspired by some previous works, in particular the ones of Luo et al. <ref name=luo2012lowdimensional> Luo, C. & Yu, B. Low dimensional simplex evolution: a new heuristic for global optimization Journal of Global Optimization, 2012, 52, 45-55</ref> <ref name=luo_modifications_2013>Luo, C.; Zhang, S.-L. & Yu, B. Some modifications of low-dimensional simplex evolution and their convergence Optimization Methods and Software, 2013, 28, 54-81 </ref> on Low dimensional simplex evolution (LDSE).
 
APS has been inspired by some previous works, in particular the ones of Luo et al. <ref name=luo2012lowdimensional> Luo, C. & Yu, B. Low dimensional simplex evolution: a new heuristic for global optimization Journal of Global Optimization, 2012, 52, 45-55</ref> <ref name=luo_modifications_2013>Luo, C.; Zhang, S.-L. & Yu, B. Some modifications of low-dimensional simplex evolution and their convergence Optimization Methods and Software, 2013, 28, 54-81 </ref> on Low dimensional simplex evolution (LDSE).
  
Each phase is explained on its own page (just click on the corresponding area of the figure). After the initialisation of $N$ individuals, all the other phases are in a loop on them. In the explanation, the current individual is called $x_i$. This loop is itself repeated as long as a stop criterion is not met. As usually, the stop criterion is either a maximum number of fitness evaluations, or an error value found smaller than a predefined threshold.
+
Each phase is explained on its own page (just click on the corresponding area of the figure). After the initialisation of $N$ individuals, all the other phases are in a loop on them. In the explanation, the current individual is called $x_i$. This loop is repeated as long as a stop criterion is not met. As usually, the stop criterion is either a maximum number of fitness evaluations, or an error value found smaller than a predefined threshold.
  
 
<references/>
 
<references/>

Latest revision as of 09:21, 23 June 2014

APS has been inspired by some previous works, in particular the ones of Luo et al. [1] [2] on Low dimensional simplex evolution (LDSE).

Each phase is explained on its own page (just click on the corresponding area of the figure). After the initialisation of $N$ individuals, all the other phases are in a loop on them. In the explanation, the current individual is called $x_i$. This loop is repeated as long as a stop criterion is not met. As usually, the stop criterion is either a maximum number of fitness evaluations, or an error value found smaller than a predefined threshold.

  1. Luo, C. & Yu, B. Low dimensional simplex evolution: a new heuristic for global optimization Journal of Global Optimization, 2012, 52, 45-55
  2. Luo, C.; Zhang, S.-L. & Yu, B. Some modifications of low-dimensional simplex evolution and their convergence Optimization Methods and Software, 2013, 28, 54-81