Oriented Crossover in Genetic Algorithms for Computer Networks Optimization

Optimization using genetic algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover an essential operator of the algorithm. It has been active area research to develop sustainable forms for this operand. In work, new operand proposed. This depends on giving elicited description chromosome with structure alleles parents. suggested that each allele two attitudes, one attitude differs contrastingly other, and both them complement allele. Thus, case where good, other should be bad. suitable many systems which contain admired parameters unadmired parameters. proposed would improve desired attitudes dampen undesired attitudes. can achieved stages: first stage mating method parent improving at expense other. second comes after improvement between different Hence, concurrent steps applied. Simulation experiments system show fitness function. could helpful fields, especially optimize routing network protocols, application tested as study work.