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Full-Text Articles in Computer Engineering
Cooperative Ant Colony Algorithm Combining Evaluation Reward And Punishment Mechanism And Neighborhood Dynamic Degradation, Yujie Wang, Xiaoming You, Sheng Liu
Cooperative Ant Colony Algorithm Combining Evaluation Reward And Punishment Mechanism And Neighborhood Dynamic Degradation, Yujie Wang, Xiaoming You, Sheng Liu
Journal of System Simulation
Abstract: To address the slow convergence and the tendency to fall into local optimality in solving TSP, a cooperative ant colony algorithm combining evaluation reward and punishment mechanism and neighborhood dynamic degradation (ENCACO) is proposed. The paths are classified into active and abandon paths according to the path evaluation value, and with the path evaluation value as the weight, the different pheromone reward and punishment strategies are adopted for the two types of paths to accelerate the convergence speed of the algorithm. Through the neighborhood dynamic degradation strategy, and the neighborhood radius is used to divide the set of cities …
Meta-Genetic Programming: Co-Evolving The Operators Of Variation, Bruce Edmonds
Meta-Genetic Programming: Co-Evolving The Operators Of Variation, Bruce Edmonds
Turkish Journal of Electrical Engineering and Computer Sciences
The standard Genetic Programming approach is augmented by co-evolving the genetic operators. To do this the operators are coded as trees of indefinite length. In order for this technique to work, the language that the operators are defined in must be such that it preserves the variation in the base population. This technique can varied by adding further populations of operators and changing which populations act as operators for others, including itself, thus to provide a framework for a whole set of augmented GP techniques. The technique is tested on the parity problem. The pros and cons of the technique …