Applying Boosting Techniques to Genetic Programming

Paris, Grégory; Robilliard, Denis; Fonlupt, Cyril

doi:10.1007/3-540-46033-0_22

Cited by 33 publications

(25 citation statements)

References 8 publications

(7 reference statements)

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“…Boosting practices have been effectively used to enhance the functioning of other recognized processes from the Machine Learning field [12], such as ANN, and GP. Su et al [13] explained the neural network techniques in projects from mathematical point of view of software reliability modeling.…”

Section: 'Smentioning

confidence: 99%

Software Reliability Modeling: Comparative Analysis

Malhotra

2017

IJARCSSE

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-This paper includes the software reliability prediction and estimation as an area of software development process which needs to specify predict, estimate and asses the software reliability system. Software trustworthiness is an significant part of software quality along with functionality, performance, usability, maintainability, serviceability including many ones. Software reliability related to the software quality and directly affects the system reliability. There are hundreds of models developed from the last thirty to forty years to improve the software trustworthiness. The aim of this piece is to investigation of different software reliability models that can improve the trustworthiness of the software, their classification and finding the best fit for a particular project.

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Section: 'Smentioning

confidence: 99%

Software Reliability Modeling: Comparative Analysis

Malhotra

2017

IJARCSSE

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“…It has been extensively used in neural networks (e.g., [7,14,17,18,35]) and even more extensively in boosting and machine learning in general (albeit, mostly for classification). See [2,4,5,8,22,24,29] for examples. Krogh and Vedelsby [14] presented the idea of using disagreement of ensemble models for quantifying the ambiguity of ensemble prediction for neural networks, but the approach has not been adapted to symbolic regression.…”

Section: Ensemble Selectionmentioning

confidence: 99%

Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization

Veeramachaneni¹,

Vladislavleva²,

O’Reilly³

2012

Genet Program Evolvable Mach

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Knowledge mining sensory evaluation data is a challenging process due to extreme sparsity of the data, and a large variation in responses from different members (called assessors) of the panel. The main goals of knowledge mining in sensory sciences are understanding the dependency of the perceived liking score on the concentration levels of flavors' ingredients, identifying ingredients that drive liking, segmenting the panel into groups with similar liking preferences and optimizing flavors to maximize liking per group. Our approach employs (1) Genetic programming (symbolic regression) and ensemble methods to generate multiple diverse explanations of assessor liking preferences with confidence information; (2) statistical techniques to extrapolate using the produced ensembles to unobserved regions of the flavor space, and segment the assessors into groups which either have the same propensity to like flavors, or are driven by the same ingredients; and (3) two-objective swarm optimization to identify flavors which are well and consistently liked by a selected segment of assessors.

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“…The resulting solutions were found to represent sets of weak learners making post-training evaluation of team member roles difficult [22,23]. Other approaches evolved ensembles of learners using an island approach [10,20] in which mechanisms for diversity maintenance (and thus cooperation) were not as direct as they are in an explicitly coevolutionary model. Moreover, these approaches do not scale to producing large teams or training on large datasets since they require one run to generate each individual in the final solution.…”

Section: Introductionmentioning

confidence: 99%

Managing team-based problem solving with symbiotic bid-based genetic programming

Lichodzijewski

Heywood

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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Bid-based Genetic Programming (GP) provides an elegant mechanism for facilitating cooperative problem decomposition without an a priori specification of the number of team members. This is in contrast to existing teaming approaches where individuals learn a direct input-output map (e.g., from exemplars to class labels), allowing the approach to scale to problems with multiple outcomes (classes), while at the same time providing a mechanism for choosing an outcome from those suggested by team members. This paper proposes a symbiotic relationship that continues to support the cooperative bid-based process for problem decomposition while making the credit assignment process much clearer. Specifically, team membership is defined by a team population indexing combinations of GP individuals in a separate team member population. A Pareto-based competitive coevolutionary component enables the approach to scale to large problems by evolving informative test points in a third population. The ensuing Symbiotic Bid-Based (SBB) model is evaluated on three large classification problems and compared to the XCS learning classifier system (LCS) formulation and to the support vector machine (SVM) implementation LIBSVM. On two of the three problems investigated the overall accuracy of the SBB classifiers was found to be competitive with the XCS and SVM results. At the same time, on all problems, the SBB classifiers were able to detect instances of all classes whereas the XCS and SVM models often ignored exemplars of minor classes. Moreover, this was achieved with a level of model complexity significantly lower than that identified by the SVM and XCS solutions.

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Applying Boosting Techniques to Genetic Programming

Cited by 33 publications

References 8 publications

Software Reliability Modeling: Comparative Analysis

Software Reliability Modeling: Comparative Analysis

Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization

Managing team-based problem solving with symbiotic bid-based genetic programming

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