Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Ideal Evaluation from Coevolution
Evolutionary Computation
Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A comparison of evaluation methods in coevolution
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Novelty detection + coevolution = automatic problem decomposition: a framework for scalable genetic programming classifiers
Training binary GP classifiers efficiently: a Pareto-coevolutionary approach
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
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
Classification as clustering: A pareto cooperative-competitive gp approach
Evolutionary Computation
Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces
Genetic Programming and Evolvable Machines
Novelty-Based fitness: an evaluation under the santa fe trail
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
GP under streaming data constraints: a case for pareto archiving?
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Benchmarking pareto archiving heuristics in the presence of concept drift: diversity versus age
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
Pareto competitive models of coevolution have the potential to provide a number of distinct advantages over the canonical approach to training under the Genetic Programming (GP) classifier domain. Recent work has specifically focused on the reformulation of training as a two-population competition, that is learners versus training exemplars. Such a scheme affords, for example, the capacity to decouple the fitness evaluation overhead from the data set size through sub sampling while naturally encouraging 'teams' or composite solutions as opposed to solutions based on a single individual alone. One outstanding question with respect to the latter characteristic is with regards to the nature of the team (archive) behavior in terms of pattern coverage. That is to say, which models are used when, and what are the implications for solution modularity as it relates, for example, to the assignment of exemplars to solution participants. The current work investigates two Pareto competitive approaches to classification under GP, with one configured to employ an explicitly cooperative multi-objective cost function based and the other employing the classical (error-based) cost function. We empirically demonstrate a critical distinction between the two with regards to problem decomposition, with the capacity to provide a decomposition into unique behaviors being much more prevalent when co-operative mechanisms are explicitly supported.