C4.5: programs for machine learning
C4.5: programs for machine learning
Convergence characteristics of keep-best reproduction
Proceedings of the 1999 ACM symposium on Applied computing
Machine Learning
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
Evolution of mathematical models of chaotic systems based on multiobjective genetic programming
Knowledge and Information Systems
Behavioural GP diversity for dynamic environments: an application in hedge fund investment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Dynamic integration of classifiers for handling concept drift
Information Fusion
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Time Series Forecasting for Dynamic Environments: The DyFor Genetic Program Model
IEEE Transactions on Evolutionary Computation
Estimating the difficulty level of the challenges proposed in a competitive e-learning environment
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
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This paper investigates the feasibility of using Genetic Programming in dynamically changing environments to evolve decision trees for classification problems and proposes an new version of Genetic Programming called Adaptive Genetic Programming. It does so by comparing the performance or classification error of Genetic Programming and Adaptive Genetic Programming to that of Gradient Descent in abruptly and progressively changing environments. To cope with dynamic environments, Adaptive Genetic Programming incorporates adaptive control parameters, variable elitism and culling. Results show that both Genetic Programming and Adaptive Genetic Programming are viable algorithms for dynamic environments yielding a performance gain over Gradient Descent for lower dimensional problems even with severe environment changes. In addition, Adaptive Genetic Programming performs slightly better than Genetic Programming, due to faster recovery from changes in the environment.