Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
A less destructive, context-aware crossover operator for GP
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Solving traveling salesman problems by artificial immune response
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.