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Evolutionary Programming Made Faster

Test Suite for the Special Issue of Soft Computing on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems

## Schwefel's 2.21

## Two dimensional view |
## Fine grain view |

## Function

## Latex

A minimization problem:

$$f(x_0 \cdots x_n) = max_i\{|x_i|, 1 \leq i \leq n \}$$

$$-100 \leq x_i \leq 100$$

$$\text{minimum at }f(0, \cdots, 0) = 0$$

## Python

def fitnessFunc(self, chromosome): """""" maximum = 0.0 for c in chromosome: if abs(c) > maximum: maximum = abs(c) return maximum

## Sources

The following may or may not contain the originator of this function.Evolutionary Programming Made Faster

@ARTICLE{771163, author={Xin Yao and Yong Liu and Guangming Lin}, journal={Evolutionary Computation, IEEE Transactions on}, title={Evolutionary programming made faster}, year={1999}, month={jul}, volume={3}, number={2}, pages={82 -102}, keywords={Cauchy mutation;combinatorial optimization problems;convergence rates;evolutionary programming;global optimum;local minima;multimodal functions;numerical optimization problems;primary search operator;search step size;unimodal functions;convergence;evolutionary computation;optimisation;probability;search problems;}, doi={10.1109/4235.771163}, ISSN={1089-778X},}

Test Suite for the Special Issue of Soft Computing on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems

@article{herrera2010test, title={Test suite for the special issue of soft computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems}, author={Herrera, F. and Lozano, M. and Molina, D.}, journal={Last accessed: July}, year={2010} }