Hybrid Initialization in the Process of Evolutionary Learning

Łapa, Krystian; Cpałka, Krzysztof; Hayashi, Yuzo

doi:10.1007/978-3-319-59063-9_34

Cited by 4 publications

(1 citation statement)

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“…Population-based methods do not depend on random number generators and number transformation methods. Due to this, they can be combined with other types of initialization methods [ 51 ]. In this work, such combinations have been tested with an idea to produce different initial values (e.g., initialize smaller values and examine the effect of such initialization on the obtained results).…”

Section: Multi-objective Evolutionary Instance Selection For Regrementioning

confidence: 99%

Multi-Objective Evolutionary Instance Selection for Regression Tasks

Kordos

Łapa

2018

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The purpose of instance selection is to reduce the data size while preserving as much useful information stored in the data as possible and detecting and removing the erroneous and redundant information. In this work, we analyze instance selection in regression tasks and apply the NSGA-II multi-objective evolutionary algorithm to direct the search for the optimal subset of the training dataset and the k-NN algorithm for evaluating the solutions during the selection process. A key advantage of the method is obtaining a pool of solutions situated on the Pareto front, where each of them is the best for certain RMSE-compression balance. We discuss different parameters of the process and their influence on the results and put special efforts to reducing the computational complexity of our approach. The experimental evaluation proves that the proposed method achieves good performance in terms of minimization of prediction error and minimization of dataset size.

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Section: Multi-objective Evolutionary Instance Selection For Regrementioning

confidence: 99%