抄録
In this paper, we examine the effect of using a non-geometric binary crossover operator in genetic algorithms through computational experiments on some test problems including a GECCO 2007 worst one-max solver competition task.
Whereas standard binary crossover generates an offspring in the segment between its two parents under the Hamming distance in the genotype space (i.e., the sum of the distances from the generated offspring to its two parents is equal to the distance between the two parents), non-geometric crossover generates an offspring outside the segment between its two parents.
We demonstrate that non-geometric crossover can slow down the evolution toward the optimal solution through computational experiments on the worst one-max solver competition task.
Its usefulness as a diversity maintenance mechanism is also shown for a knapsack problem and a function optimization problem of Schwefel function.
