dr Tomasz D.Gwiazda
Assistant Professor

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Standard operators

Binary coded operators
 Elitist Crossover (EX) download PDF with first 40 pages from my latest eBook if you need more operators click here Keywords selection, exploration, exploitation, exploration/exploitation balance, competition for survival Motivation ●   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              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 WEB:     ●   Coli M., Genusso P., Palazzari P. (1996), A New Crossover Operator for Genetic Algorithms, in IEEE International Conference on Evolutionary Computation, pp. 201-206 WEB:     See also ●   Best Schema Crossover ●   Selective Crossover-2 ●   Partially Randomized Crossover ●   Direct Design Variable Exchange Crossover Algorithm 1.        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:                V1=Ai(t)XAi+1(t)      V2=Ai(t)XAi+1(t)  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 where: X – preferred crossover method Comments ●   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