Difference between revisions of "Presentation"

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Here will be the general presentation of standard APS.
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Image:Flow_chart.png|right|thumb|280px|Standard APS flow chart
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rect 6 5 368 122 [[Initialisation|Initialisation]]
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rect 198 234 563 346 [[Selection|Selection]]
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rect 199 426 562 539 [[Expansion|Expansion]]
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rect 198 617 563 732 [[Contraction|Contraction]]
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rect 197 810 564 925 [[Local search|Local search]]
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rect 134 547 268 565 [[Improvement|Improvement]]
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rect 133 738 268 752 [[Improvement|Improvement]]
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desc none
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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).
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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.
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<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