Home

A novel distributed genetic algorithm implementation with variable number of islands

The influence of migration sizes and intervals on island models

Global Optimization

Evaluating evolutionary algorithms

This benchmark is listed as F6 in "The parallel genetic algorithm as function optimizer"

## Rastrigin

## Two dimensional view |
## One dimensional view |

## Fine grain view |

## Function

## Latex

A minimization problem:

$$f(x_1 \cdots x_n) = 10n + \sum_{i=1}^n (x_i^2 -10cos(2\pi x_i))$$

$$-5.12 \leq x_i \leq 5.12$$

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

## Python

def fitnessFunc(self, chromosome): """F5 Rastrigin's function multimodal, symmetric, separable""" fitness = 10*len(chromosome) for i in range(len(chromosome)): fitness += chromosome[i]**2 - (10*math.cos(2*math.pi*chromosome[i])) return fitness

## Sources

The following may or may not contain the originator of this function.A novel distributed genetic algorithm implementation with variable number of islands

@inproceedings{varIslandNum07, author = {Takuma Jumonji and Goutam Chakraborty and Hiroshi Mabuchi and Masafumi Matsuhara}, title = {A novel distributed genetic algorithm implementation with variable number of islands}, booktitle = {IEEE Congress on Evolutionary Computation}, year = {2007}, pages = {4698--4705}, doi = {10.1109/CEC.2007.4425088}, masid = {4737000} }

The influence of migration sizes and intervals on island models

@inproceedings{Skolicki:2005:IMS:1068009.1068219, author = {Skolicki, Zbigniew and De Jong, Kenneth}, title = {The influence of migration sizes and intervals on island models}, booktitle = {Proceedings of the 2005 conference on Genetic and evolutionary computation}, series = {GECCO '05}, year = {2005}, isbn = {1-59593-010-8}, location = {Washington DC, USA}, pages = {1295--1302}, numpages = {8}, url = {http://doi.acm.org/10.1145/1068009.1068219}, doi = {10.1145/1068009.1068219}, acmid = {1068219}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {evolutionary computation, island model, migration, migration interval, migration size, test suite}, }

Global Optimization

@article{torn1989global, title={Global Optimization}, author={Torn, A. and Zilinskas, A.}, year={1989}, journal={Lecture Notes in Computer Science}, number={350} }

Evaluating evolutionary algorithms

@article{Whitley1996245, title = "Evaluating evolutionary algorithms", journal = "Artificial Intelligence", volume = "85", number = "1-2", pages = "245 - 276", year = "1996", note = "", issn = "0004-3702", doi = "10.1016/0004-3702(95)00124-7", url = "http://www.sciencedirect.com/science/article/pii/0004370295001247", author = "Darrell Whitley and Soraya Rana and John Dzubera and Keith E. Mathias" }

This benchmark is listed as F6 in "The parallel genetic algorithm as function optimizer"

@article{Muhlenbein1991619, title = "The parallel genetic algorithm as function optimizer", journal = "Parallel Computing", volume = "17", number = "6-7", pages = "619 - 632", year = "1991", note = "", issn = "0167-8191", doi = "10.1016/S0167-8191(05)80052-3", url = "http://www.sciencedirect.com/science/article/pii/S0167819105800523", author = "H. M{\"u}hlenbein and M. Schomisch and J. Born", keywords = "Search methods", keywords = "optimization methods", keywords = "parallel genetic algorithm", keywords = "performance evaluation", keywords = "speedup results" }