Difference between revisions of "Presentation"
From Adaptive Population based Simplex
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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]] | ||
| + | rect 198 234 563 346 [[Selection|Selection]] | ||
| + | rect 199 426 562 539 [[Expansion|Expansion]] | ||
| + | rect 198 617 563 732 [[Contraction|Contraction]] | ||
| + | rect 197 810 564 925 [[Local search|Local search]] | ||
| + | rect 134 547 268 565 [[Improvement|Improvement]] | ||
| + | rect 133 738 268 752 [[Improvement|Improvement]] | ||
| + | desc none | ||
| + | </imagemap> | ||
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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.
- ↑ Luo, C. & Yu, B. Low dimensional simplex evolution: a new heuristic for global optimization Journal of Global Optimization, 2012, 52, 45-55
- ↑ 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
