dr Tomasz D.Gwiazda
 Assistant Professor

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


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
    Random Respectful Crossover  




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, similarity

   Offspring generation from a similarity set of the parents.

Source text
Radcliffe N.J.  (1991), Forma Analysis and Random Respectful Recombination, in Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 222-229
WEB:     http://users.breathe.com/njr/formaPapers.html


Read also
 Watson R.A., Pollack J.B.  (2000), Recombination Without Respect: Schema Combination and Disruption in Genetic Algorithm Crossover, in Proceedings of GECCO 2000, Morgan Kaufman, pp. 112-119

   Ozugur T.  (2005), Hierarchical Provisioning for Cellular networks, in IEEE Transactions on Wireless Communications, vol. 4(2), pp. 775-791
WEB:      http://ieeexplore.ieee.org/xpl/abs_free.jsp?arNumber=1413243

 Nomura T.  (1997), An Analysis on Crossovers for Real Number Chromosomes in an Infinite Population Size, ATR Human Information Processing Research Laboratories, Evolutionary Systems Department
WEB:     http://citeseer.ifi.unizh.ch/62577.html


See also
    Hierarchical Crossover
    Disrespectful Crossover
    Asymmetric Two-point Crossover
    Variation of Asymmetric Two-point Crossover
    Homologous Crossover
    Schema-Based Crossover
    Adaptive Probability Crossover-4

     select two parents A(t) and B(t) from a parent pool

2.     create a similarity vector SAB=(s1AB,...,snAB) as follows:

3.              for i = 1 to n do

4.                              if ai(t)=bi(t) then

5.                              siAB=ai(t)

6.                              else

7.                              siAB=NULL

8.                              end if

9.              end do

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

11.           for i = 1 to n do

12.                           if siAB=1 then

13.                           ci(t+1)=1 

14.                           di(t+1)=1   

15.                           else if siAB=0 then

16.                           ci(t+1)=0 

17.                           di(t+1)=0  

18.                           else if siAB=NULL then

19.                           choose a uniform random real number u from interval

20.                                           if u≤0.5 then

21.                                           ci(t+1)=1 

22.                                           else

23.                                           ci(t+1)=0 

24.                                           end if

25.                           choose a uniform random real number u from interval

26.                                           if u≤0.5 then

27.                                           di(t+1)=1 

28.                                           else

29.                                           di(t+1)=0   

30.                                           end if

31.                           end if

32.           end do

   The R3 algorithm duplicates genes of parents in an offspring at every position at which they are identical (rows: 12-17). At positions where  values of the parent genes are different they are determined at random (rows: 18-31).

Experiment domains

Compared to


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