Publications · 2012 · Conference paper
Locality-Based Multiobjectivization for the HP Model of Protein Structure Prediction
In 2012 Genetic and Evolutionary Computation Conference (GECCO’2012), 2012
Abstract
In this paper, two novel evolutionary approaches for many-objective optimization are proposed. These algorithms integrate a fine-grained ranking of solutions to favor convergence, with explicit methodologies for diversity promotion in order to guide the search towards a representative approximation of the Pareto-optimal surface. In order to validate the proposed algorithms, we performed a comparative study where four state-of-the-art representative approaches were considered. In such a study, four well-known scalable test problems were adopted as well as six different problem sizes, ranging from 5 to 50 objectives. Our results indicate that our two proposed algorithms consistently provide good convergence as the number of objectives increases, outperforming the other approaches with respect to which they were compared.
BibTeX
@inproceedings{Fabre12a,
author = {Mario Garza-Fabre and Gregorio Toscano-Pulido and Eduardo Rodriguez-Tello},
title = {{Locality-Based Multiobjectivization for the HP Model of Protein Structure Prediction}},
booktitle = {2012 Genetic and Evolutionary Computation Conference (GECCO'2012)},
pages = {473--480},
publisher = {ACM Press},
address = {Philadelphia, USA},
month = {July},
year = {2012},
doi = {10.1007/978-3-642-29124-1_16},
url = {https://doi.org/10.1007/978-3-642-29124-1_16},
isbn = {978-3-642-29123-4},
}