@inproceedings{10.1145/3583133.3596437,
author = {Stapleton, Fergal and Galvan, Edgar},
title = {Initial Steps Towards Tackling High-dimensional Surrogate Modeling for Neuroevolution Using Kriging Partial Least Squares},
year = {2023},
isbn = {9798400701207},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583133.3596437},
doi = {10.1145/3583133.3596437},
abstract = {Surrogate-assisted evolutionary algorithms (SAEAs) aim to use efficient computational models with the goal of approximating the fitness function in evolutionary computation systems. This area of research has received significant attention from the specialised research community in different areas, for example, single and many objective optimisation or dynamic and stationary optimisation problems. An emergent and exciting area that has received little attention from the SAEAs community is in neuroevolution. This refers to the use of evolutionary algorithms in the automatic configuration of artificial neural network (ANN) architectures, hyper-parameters and/or the training of ANNs. However, ANNs suffer from two major issues: (a) the use of highly-intense computational power for their correct training, and (b) the highly specialised human expertise required to correctly configure ANNs necessary to get a well-performing network. This work aims to fill this important research gap in SAEAs in neuroevolution by addressing these two issues. We demonstrate how one can use a Kriging Partial Least Squares method in place of the well-known Kriging method, which normally cannot be used in neuroevolution due to the high dimensionality of the data.},
booktitle = {Proceedings of the Companion Conference on Genetic and Evolutionary Computation},
pages = {83–84},
numpages = {2},
keywords = {partial least squares, kriging, surrogate-assisted evolutionary algorithms, neural architecture search, neuroevolution},
location = {Lisbon, Portugal},
series = {GECCO '23 Companion}
}

{"_id":"efqqdWDP8GFa7WSmx","bibbaseid":"stapleton-galvan-initialstepstowardstacklinghighdimensionalsurrogatemodelingforneuroevolutionusingkrigingpartialleastsquares-2023","author_short":["Stapleton, F.","Galvan, E."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"propositions":[],"lastnames":["Stapleton"],"firstnames":["Fergal"],"suffixes":[]},{"propositions":[],"lastnames":["Galvan"],"firstnames":["Edgar"],"suffixes":[]}],"title":"Initial Steps Towards Tackling High-dimensional Surrogate Modeling for Neuroevolution Using Kriging Partial Least Squares","year":"2023","isbn":"9798400701207","publisher":"Association for Computing Machinery","address":"New York, NY, USA","url":"https://doi.org/10.1145/3583133.3596437","doi":"10.1145/3583133.3596437","abstract":"Surrogate-assisted evolutionary algorithms (SAEAs) aim to use efficient computational models with the goal of approximating the fitness function in evolutionary computation systems. This area of research has received significant attention from the specialised research community in different areas, for example, single and many objective optimisation or dynamic and stationary optimisation problems. An emergent and exciting area that has received little attention from the SAEAs community is in neuroevolution. This refers to the use of evolutionary algorithms in the automatic configuration of artificial neural network (ANN) architectures, hyper-parameters and/or the training of ANNs. However, ANNs suffer from two major issues: (a) the use of highly-intense computational power for their correct training, and (b) the highly specialised human expertise required to correctly configure ANNs necessary to get a well-performing network. This work aims to fill this important research gap in SAEAs in neuroevolution by addressing these two issues. We demonstrate how one can use a Kriging Partial Least Squares method in place of the well-known Kriging method, which normally cannot be used in neuroevolution due to the high dimensionality of the data.","booktitle":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","pages":"83–84","numpages":"2","keywords":"partial least squares, kriging, surrogate-assisted evolutionary algorithms, neural architecture search, neuroevolution","location":"Lisbon, Portugal","series":"GECCO '23 Companion","bibtex":"@inproceedings{10.1145/3583133.3596437,\nauthor = {Stapleton, Fergal and Galvan, Edgar},\ntitle = {Initial Steps Towards Tackling High-dimensional Surrogate Modeling for Neuroevolution Using Kriging Partial Least Squares},\nyear = {2023},\nisbn = {9798400701207},\npublisher = {Association for Computing Machinery},\naddress = {New York, NY, USA},\nurl = {https://doi.org/10.1145/3583133.3596437},\ndoi = {10.1145/3583133.3596437},\nabstract = {Surrogate-assisted evolutionary algorithms (SAEAs) aim to use efficient computational models with the goal of approximating the fitness function in evolutionary computation systems. This area of research has received significant attention from the specialised research community in different areas, for example, single and many objective optimisation or dynamic and stationary optimisation problems. An emergent and exciting area that has received little attention from the SAEAs community is in neuroevolution. This refers to the use of evolutionary algorithms in the automatic configuration of artificial neural network (ANN) architectures, hyper-parameters and/or the training of ANNs. However, ANNs suffer from two major issues: (a) the use of highly-intense computational power for their correct training, and (b) the highly specialised human expertise required to correctly configure ANNs necessary to get a well-performing network. This work aims to fill this important research gap in SAEAs in neuroevolution by addressing these two issues. We demonstrate how one can use a Kriging Partial Least Squares method in place of the well-known Kriging method, which normally cannot be used in neuroevolution due to the high dimensionality of the data.},\nbooktitle = {Proceedings of the Companion Conference on Genetic and Evolutionary Computation},\npages = {83–84},\nnumpages = {2},\nkeywords = {partial least squares, kriging, surrogate-assisted evolutionary algorithms, neural architecture search, neuroevolution},\nlocation = {Lisbon, Portugal},\nseries = {GECCO '23 Companion}\n}\n\n","author_short":["Stapleton, F.","Galvan, E."],"key":"10.1145/3583133.3596437","id":"10.1145/3583133.3596437","bibbaseid":"stapleton-galvan-initialstepstowardstacklinghighdimensionalsurrogatemodelingforneuroevolutionusingkrigingpartialleastsquares-2023","role":"author","urls":{"Paper":"https://doi.org/10.1145/3583133.3596437"},"keyword":["partial least squares","kriging","surrogate-assisted evolutionary algorithms","neural architecture search","neuroevolution"],"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Computer-Science-Research-MU/News/main/BIB202223/report202223.bib","dataSources":["FhSZTwdhYbXnvBCWX","Jspzw8BckMpqxabqN"],"keywords":["partial least squares","kriging","surrogate-assisted evolutionary algorithms","neural architecture search","neuroevolution"],"search_terms":["initial","steps","towards","tackling","high","dimensional","surrogate","modeling","neuroevolution","using","kriging","partial","squares","stapleton","galvan"],"title":"Initial Steps Towards Tackling High-dimensional Surrogate Modeling for Neuroevolution Using Kriging Partial Least Squares","year":2023}