dr Tomasz D.Gwiazda
 Assistant Professor

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Contents of e-Book
Index of authors
Index of experiment domains


Introduction

Standard operators
1-Point Crossover
k-Point Crossover
Shuffle Crossover
Reduced Surrogate Crossover
Uniform Crossover
Highly Disruptive Crossover,Heuristic Uniform Crossover
Average Crossover
Discrete Crossover
Flat Crossover
Heuristic Crossover,Intermediate Crossover
Blend Crossover


Binary coded operators
Random Respectful Crossover
Masked Crossover
1bit Adaptation Crossover
Multivariate Crossover
Homologous Crossover
Count-preserving Crossover
Elitist Crossover
    Highly Disruptive Crossover  
         

 

 

- Heuristic Uniform Crossover
            (HUX)

download PDF with first 40 pages
from my latest eBook

if you need more operators click here

Read also
Spears W.M., De Jong K.A.  (1991), On the Virtues of Parameterized Uniform Crossover, in Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufman, pp. 230-236
WEB:     http://citeseer.ifi.unizh.ch/spears91virtues.html

           
http://citeseer.ist.psu.edu/spears91virtues.html
●  Belea R., Beldiman L.  (2003), A new method of gene coding for a genetic algorithm designed for parametric optimization, in The Annals of “Dunarea De Jos”, University of Galati, pp. 66-71
WEB:    
http://www.ann.ugal.ro/eeai/archives/2003.htm

  Cordón O., Damas S., Santamaría J.  (2003), A CHC Evolutionary Algorithm for 3D Image Registration, in IFSA 2003, Springer-Verlag, pp. 404-411
WEB:     http://sci2s.ugr.es/publications/

            http://springerlink.metapress.com/
         
openurl.asp?genre=article&issn=0302-9743&volume=2715&spage=404

  Cotta C., Troya J.M.  (2000), Using Dynastic Exploring Recombination to Promote Diversity in Genetic Search, in Parallel Problem Solving from Nature - 6th International Conference, Springer Verlag, pp. 16-20
WEB:    
http://citeseer.ifi.unizh.ch/cotta00using.html
            
http://citeseer.ist.psu.edu/cotta00using.html

Algorithm HUX
1.
     select two parents A(t) and B(t) from a parent pool

2.     create two offspring  C(t+1)  and D(t+1)  as follows:

3.     C(t+1)  = A(t)

4.     D(t+1)  = B(t)

5.     number_of_different_genes = 0

6.              for i = 1 to n do

7.                              if ci(t+1)di(t+1) then

8.                              number_of_different_genes =
                             number
_of_different_genes  + 1

9.                              end if

10.           end do

11.           swap_counter = 0

12.           do while swap_counter ≤ number_of_different_genes/2

13.                           for i = 1 to n  do

14.                                           if ci(t+1)di(t+1) and ci(t+1)bi(t)
                                           then          

15.                                           choose a uniform random real number u
                                           from interval
<0,1>

16.                                                          if u 0.5 then (swap bits)

17.                                                          ci(t+1)=bi(t)

18.                                                          di(t+1)=ai(t) 

19.                                                          swap_counter =
                                                          swap_counter
+ 1

20.                                                          end if

21.                                           end if

22.                           end do

23.           loop

 
   

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