Comparison between two coevolutionary feature weighting algorithms in clustering
Pattern Recognition
Classification rule discovery with DE/QDE algorithm
Expert Systems with Applications: An International Journal
OEA_SAT: an organizational evolutionary algorithm for solving satisfiability problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A novel two level evolutionary approach for classification rules extraction
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A multiagent evolutionary algorithm for combinatorial optimization problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
IEEE Transactions on Evolutionary Computation
A granular agent evolutionary algorithm for classification
Applied Soft Computing
Immune clonal MO algorithm for ZDT problems
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Immune clonal strategies based on three mutation methods
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Global numerical optimization based on small-world networks
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Multi-Agent immune clonal selection algorithm based multicast routing
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Intrusion detection based on clustering organizational co-evolutionary classification
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Constrained optimization using organizational evolutionary algorithm
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Moving block sequence and organizational evolutionary algorithm for general floorplanning
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
A human-simulated immune evolutionary computation approach
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Constrained layout optimization in satellite cabin using a multiagent genetic algorithm
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
A multi-agent genetic algorithm for resource constrained project scheduling problems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Taking inspiration from the interacting process among organizations in human societies, a new classification algorithm, organizational coevolutionary algorithm for classification (OCEC), is proposed with the intrinsic properties of classification in mind. The main difference between OCEC and the available classification approaches based on evolutionary algorithms (EAs) is its use of a bottom-up search mechanism. OCEC causes the evolution of sets of examples, and at the end of the evolutionary process, extracts rules from these sets. These sets of examples form organizations. Because organizations are different from the individuals in traditional EAs, three evolutionary operators and a selection mechanism are devised for realizing the evolutionary operations performed on organizations. This method can avoid generating meaningless rules during the evolutionary process. An evolutionary method is also devised for determining the significance of each attribute, on the basis of which, the fitness function for organizations is defined. In experiments, the effectiveness of OCEC is first evaluated by multiplexer problems. Then, OCEC is compared with several well-known classification algorithms on 12 benchmarks from the UCI repository datasets and multiplexer problems. Moreover, OCEC is applied to a practical case, radar target recognition problems. All results show that OCEC achieves a higher predictive accuracy and a lower computational cost. Finally, the scalability of OCEC is studied on synthetic datasets. The number of training examples increases from 100 000 to 10 million, and the number of attributes increases from 9 to 400. The results show that OCEC obtains a good scalability.