dr Tomasz D.Gwiazda
Assistant Professor

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Standard operators

Binary coded operators
 Multivariate Crossover (MC) download PDF with first 40 pages from my latest eBook if you need more operators click here Keywords variable-to-variable recombination Motivation ●   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 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 Algorithm 1.     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)), B(t)=(sB1(t),...,sBq(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 where: X - standard 1-Point Crossover Rnd  uniform random real number, 0≤Rnd≤1 Comments ●  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