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Crossover methods are important keys to the success of genetic algorithms. However, traditional crossover methods fail to solve a trap problem, which is a difficult benchmark problem designed to deceive genetic algorithms to favor all-zero bits, while the actual solution is all-one bits. The Bayesian optimization algorithm (BOA) is the most famous algorithm that can solve the trap problem; however,...
Bayesian Networks (BNs) are graphical probabilistic models that represent relationships that may exist between variables of a dataset. BN can be applied to data in a variety of different ways. Yet, using a BN requires knowing its structure. BN structure learning represents a challenge as the number of possible structures is very large. Search and score approaches have been used to address the problem...
In this paper, we propose an optimization algorithm inspired by the immune response mechanism and apply it to traveling salesman problem (TSP). We test the proposed algorithm by the simulations on randomly generated 100 data sets of 10-city problems and two of TSPLIB benchmark problems: ulysses22 (22-city) and eil51 (51-city). The simulation results illustrate that the proposed algorithm can find...
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