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
    Multivariate Crossover  




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variable-to-variable recombination

   Effective optimization of multivariate functions.

Source text
   Konstam A.H., Hartley S.J., Carr W.L. (1992), Optimization in a Distributed Processing Environment using Genetic Algorithm with Multivariate Crossover, in Proceedings of the 1992 ACM annual conference on Communications, pp. 109-116
WEB:     http://portal.acm.org/citation.cfm?id=131228

Read also
   Yang S.Y., Park L.-J., Park C.H., Ra J.W. (1995), A Hybrid Algorithm using Genetic Algorithm and Gradient-Based Algorithm for Iterative Microwave Inverse Scattering, in  IEEE International Conference on Evolutionary Computation, pp.450-455
WEB:     http://intl.ieeexplore.ieee.org/xpl/abs_free.jsp?arNumber=489190

   Deb K., Goyal M., Optimizing Engineering Designs Using a Combined Genetic Search, in Proceedings of the Seventh International Conference on Genetic Algorithms, Morgan Kaufman, pp. 521-528
WEB:     http://citeseer.ifi.unizh.ch/deb95optimizing.html


See also
   Chromosome Shuffling
   2N-Parent Parameter Wise Crossover

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

2.     assume that each parent vector is divided into  q  substrings sij(t),
    where q is the number of parameters represented in each parent
    vector i.e. each sij(t) (i=A,B; j=1,...,q) represents a  jth parameter;
    hence A(t)=(sA1(t),...,sAq(t)),

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

4.              for j = 1 to q do

5.                             if Rnd pc then

6.                             perform crossover between sAj(t) and sBj(t)

7.                             sCj(t+1)=sAj(t) X sBj(t)

8.                             sDj(t+1)=sAj(t) X sBj(t)

9.                             else

10.                           sCj(t+1)=sAj(t)  

11.                           sDj(t+1)=sBj(t)  

12.                           end if

13.           end do

X - standard 1-Point Crossover

Rnd – uniform random real number, 0≤Rnd≤1

  The most fundamental difference between the MC operator and other operators using variable-to-variable recombination is that the answer to the question “whether to crossover” is checked in the MC method separately for each substring (row: 5). As for the other operators, the answer to that question refers to the parent vector as a whole.

Experiment domains
   function taken from the National Crime Survey

Compared to
   k-Point Crossover


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