Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Massively parallel genetic programming
Advances in genetic programming
Foundations of genetic programming
Foundations of genetic programming
Evolving Teams of Predictors with Linear Genetic Programming
Genetic Programming and Evolvable Machines
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Behavioral Diversity and a Probabilistically Optimal GP Ensemble
Genetic Programming and Evolvable Machines
Recent trends in learning classifier systems research
Advances in evolutionary computing
For real! XCS with continuous-valued inputs
Evolutionary Computation
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Ideal Evaluation from Coevolution
Evolutionary Computation
Dynamic Subset Selection Based on a Fitness Case Topology
Evolutionary Computation
MOGE: GP classification problem decomposition using multi-objective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Pareto-coevolutionary genetic programming classifier
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Bounding XCS's parameters for unbalanced datasets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Novel ways of improving cooperation and performance in ensemble classifiers
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Tournament selection: stable fitness pressure in XCS
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Cooperative evolution on the intertwined spirals problem
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
GP classifier problem decomposition using first-price and second-price auctions
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Training binary GP classifiers efficiently: a Pareto-coevolutionary approach
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Linear Genetic Programming
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
GP ensembles for large-scale data classification
IEEE Transactions on Evolutionary Computation
Biasing Coevolutionary Search for Optimal Multiagent Behaviors
IEEE Transactions on Evolutionary Computation
Scaling Genetic Programming to Large Datasets Using Hierarchical Dynamic Subset Selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Managing team-based problem solving with symbiotic bid-based genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Classifying SSH encrypted traffic with minimum packet header features using genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces
Genetic Programming and Evolvable Machines
Parallel linear genetic programming for multi-class classification
Genetic Programming and Evolvable Machines
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In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significant departure from models where each individual constructs a direct input-outcome map, for example, from the set of exemplars to the set of class labels as is typical under the classification domain. In contrast, the proposed model focuses on learning a bidding strategy based on the exemplar feature vectors; each individual is associated with a single discrete action and the individual with the maximum bid `wins' the right to suggest its action. Thus, the number of individuals associated with each action is a function of the intra-action bidding behaviour. Credit assignment is designed to reward correct but unique bidding strategies relative to the target actions. An advantage of the model over other teaming methods is its ability to automatically determine the number of and interaction between cooperative team members. The resulting model shares several traits with learning classifier systems and as such both approaches are benchmarked on nine large classification problems. Moreover, both of the evolutionary models are compared against the deterministic Support Vector Machine classification algorithm. Performance assessment considers the computational, classification, and complexity characteristics of the resulting solutions. The bid-based model is found to provide simple yet effective solutions that are robust to wide variations in the class representation. Support Vector Machines and classifier systems tend to perform better under balanced datasets albeit resulting in black-box solutions.