A Dynamic Programming Approach to Individual Initialization in Genetic Programming

Kren, Toma; Neruda, Roman

doi:10.1109/smc.2015.307

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B Generating Typed Trees With Parametric Polymorphism1

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2015

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“…The presented algorithm is a generalization for polymorphously typed GP of our previous work on individual generating introduced in [10].…”

Section: B Generating Typed Trees With Parametric Polymorphismmentioning

confidence: 99%

Evolving Workflow Graphs Using Typed Genetic Programming

Ken

Pilát

Neruda

2015

2015 IEEE Symposium Series on Computational Intelligence

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When applying machine learning techniques to more complicated datasets, it is often beneficial to use ensembles of simpler models instead of a single, more complicated, model. However, the creation of ensembles is a tedious task which requires a lot of human interaction and experimentation. In this paper, we present a technique for construction of ensembles based on typed genetic programming. The technique describes an ensemble as a directed acyclic graph, which is internally represented as a tree evolved by the genetic programming. The approach is evaluated in a series of experiments on various datasets and compared to the performance of simple models tuned by grid search, as well as to ensembles generated in a systematic manner.

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“…The presented algorithm is a generalization for polymorphously typed GP of our previous work on individual generating introduced in [10].…”

Section: B Generating Typed Trees With Parametric Polymorphismmentioning

confidence: 99%