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
    Elitist Crossover  




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, exploration
, exploitation, exploration/exploitation balance, competition for survival

   Assessing the effectivity of integrating the selection and crossover processes.

Source text
   Thierens D., Goldberg D.E. (1994), Elitist Recombination: an integrated selection recombination GA, in Proceedings of the First IEEE World Congress on Computational Intelligence, pp. 508-512
WEB:      http://intl.ieeexplore.ieee.org/xpl/abs_free.jsp?arNumber=349898


Read also
   Vekaria K., Clack C. (1998), Selective Crossover in Genetic Algorithms: An Empirical Study, in Proceedings of the fifth Conference on Parallel Problem Solving from Nature, Springer-Verlag, pp. 438-447

   Coli M., Genusso P., Palazzari P. (1996), A New Crossover Operator for Genetic Algorithms, in IEEE International Conference on Evolutionary Computation, pp. 201-206

See also
   Best Schema Crossover
   Selective Crossover-2
   Partially Randomized Crossover
   Direct Design Variable Exchange Crossover

        for every generation of GA do

2.        randomly shuffle the entire population P(t)={A1(t),...,APopulation_size(t)}

3.                for i = 1 to Population_size  do

4.                create two vectors V1 and V2:

5.                compute the fitness value of V1 and V2

6.                insert best two vectors of {Ai(t),Ai+1(t),V1,V2} into the next
               population P(t+1) as offspring

7.                i = i + 2

8.                end do

X – preferred crossover method

   In the standard genetic algorithm, the selection process is always preceded by the crossover process. In the EX method both of the processes are integrated. During the first step the entire population is randomly shuffled (row: 2). Then, from each successive pair of parental vectors, two new vectors are created by crossover (row: 4). From a “family” created in this way, two best vectors are singled out and implemented as offspring to the next population (row: 6).
   Application of elitist selection in the traditional way that is on the level of the entire population may often be the reason for the premature convergence of the algorithm. An EX elitist selection applied on the “family” level (row: 6) eliminates this danger according to the authors.

Experiment domains
   bit counting function
   fully deceptive trap function

Compared to
   Uniform Crossover


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