Crossover Operator Assignment Help

A Simple Genetic Algorithm - Crossover Operator

Crossover Operator

At every generation the genetic operations mimic the process of heredity of genes to create offspring. The evolution operation mimics the process of Darwinian evolution to generate populations from generation to generation. It is the major genetic operator. This operates on two chromosomes at a time and generates offspring by combining both chromosome features. An easy way to achieve crossover would be to select a random cut-point and generate the offspring by combining the segment of one parent to left of the cut-point along with the segment of the other parent to the right of the cut-point. This method works well along with the bit string representation as displayed in diagram. The performance of genetic algorithm depends to a huge extent, on the performance of the crossover operator utilized.

The crossover rate or pc is defined like the ratio of the number of offspring produced in all generation to the population size (pop_size). Such ration controls the expected number pc × pop_size of chromosomes to undergo the crossover process. A higher crossover rate permits exploration of more of the solution space and decreases the chances of settling for a false optimum; however if this rate is too high, this results in the wastage of a lot of computation time in exploring unpromising regions of the resolution space.


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                             Diagram of: Crossover Operation to Generate Offspring

Mutation Operator Penalty Function

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