Multi-Objective Optimisation of Multi-Output Neural Trees
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- @InProceedings{Ojha:2020:CEC,
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author = "Varun Ojha and Giuseppe Nicosia",
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booktitle = "2020 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Multi-Objective Optimisation of Multi-Output Neural
Trees",
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year = "2020",
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editor = "Yaochu Jin",
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month = "19-24 " # jul,
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-7281-6929-3",
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DOI = "doi:10.1109/CEC48606.2020.9185600",
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abstract = "We propose an algorithm and a new method to tackle the
classification problems. We propose a multi-output
neural tree (MONT) algorithm, which is an evolutionary
learning algorithm trained by the non-dominated sorting
genetic algorithm (NSGA)-III. Since evolutionary
learning is stochastic, a hypothesis found in the form
of MONT is unique for each run of evolutionary
learning, i.e., each hypothesis (tree) generated bears
distinct properties compared to any other hypothesis
both in topological space and parameter-space. This
leads to a challenging optimisation problem where the
aim is to minimise the tree-size and maximise the
classification accuracy. Therefore, the
Pareto-optimality concerns were met by hypervolume
indicator analysis. We used nine benchmark
classification learning problems to evaluate the
performance of the MONT. As a result of our
experiments, we obtained MONTs which are able to tackle
the classification problems with high accuracy. The
performance of MONT emerged better over a set of
problems tackled in this study compared with a set of
well-known classifiers: multilayer perceptron,
reduced-error pruning tree, naive Bayes classifier,
decision tree, and support vector machine. Moreover,
the performances of three versions of MONT's training
using genetic programming, NSGA-II, and NSGA-III
suggests that the NSGA-III gives the best
Pareto-optimal solution.",
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notes = "Also known as \cite{9185600}",
- }
Genetic Programming entries for
Varun Kumar Ojha
Giuseppe Nicosia
Citations