ICCS 2007: Computational Science – ICCS 2007 pp 1061-1067 | Cite as
Towards a Less Destructive Crossover Operator Using Immunity Theory
Abstract
When searching for good scheme, a good solution can be destroyed by an inappropriate choice of crossover points. Furthermore, because of the randomicity of crossover, mutation and selection, a better solution can hardly reach in last stage in EA, and the solution always traps in local optimal. Faced to “exploding” solution space, it is tough to find high quality solution just by increasing the population size, diversity of searching, and the number of iteration. In this paper, we design the immunity operator to improve the crossover result by utilizing the immunity theory. As the “guided mutation operator”, the immunity operator substituted the “blind mutation operator” in normal EA, to restrain the degenerate phenomenon during the evolutionary process. We examine the algorithm with examples of TSP and gain promising result.
Keywords
Crossover operator Immunity operator Traveling salesman problemReferences
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