Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
Explanatory Analysis of the Metabolome Using Genetic Programming of Simple, Interpretable Rules
Genetic Programming and Evolvable Machines
Journal of Global Optimization
Machine Learning
Particle swarm based Data Mining Algorithms for classification tasks
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
A hybrid PSO/ACO algorithm for discovering classification rules in data mining
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Quantum-Inspired Differential Evolution for Binary Optimization
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Flexible learning of problem solving heuristics through adaptive search
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
An organizational coevolutionary algorithm for classification
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
Expert Systems with Applications: An International Journal
Optimized distance metrics for differential evolution based nearest prototype classifier
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
The quantum-inspired differential evolution algorithm (QDE) is a new optimization algorithm in the binary-valued space. The paper proposes the DE/QDE algorithm for the discovery of classification rules. DE/QDE combines the characteristics of the conventional DE algorithm and the QDE algorithm. Based on some strategies of DE and QDE, DE/QDE can directly cope with the continuous, nominal attributes without discretizing the continuous attributes in the preprocessing step. DE/QDE also has specific weight mutation for managing the weight value of the individual encoding. Then DE/QDE is compared with Ant-Miner and CN2 on six problems from the UCI repository datasets. The results indicate that DE/QDE is competitive with Ant-Miner and CN2 in term of the predictive accuracy.