How entropy-theorems can show that approximating high-dim Pareto-fronts is too hard
It is empirically established that multiobjective evolutionary algorithms do not scale well with the number of conflicting objectives. We here show that the convergence rate of any comparison-based multi-objective algorithm, for the Hausdorff distance, is not much better than the convergence rate of...
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ftepunivpsaclay:oai:HAL:hal-00113362v1 2023-05-15T16:49:01+02:00 How entropy-theorems can show that approximating high-dim Pareto-fronts is too hard Teytaud, Olivier Algorithmic number theory for cryptology (TANC) Laboratoire d'informatique de l'École polytechnique Palaiseau (LIX) École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) Reykjavik, Iceland 2006 https://hal.science/hal-00113362 https://hal.science/hal-00113362/document https://hal.science/hal-00113362/file/pareto2.pdf en eng HAL CCSD hal-00113362 https://hal.science/hal-00113362 https://hal.science/hal-00113362/document https://hal.science/hal-00113362/file/pareto2.pdf info:eu-repo/semantics/OpenAccess Bridging the Gap between Theory and Practice - Workshop PPSN-BTP https://hal.science/hal-00113362 Bridging the Gap between Theory and Practice - Workshop PPSN-BTP, 2006, Reykjavik, Iceland Multi-Objective Optimization Evolutionary Computation Theory [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA] [SCCO.COMP]Cognitive science/Computer science info:eu-repo/semantics/conferenceObject Conference papers 2006 ftepunivpsaclay 2023-03-15T17:35:34Z It is empirically established that multiobjective evolutionary algorithms do not scale well with the number of conflicting objectives. We here show that the convergence rate of any comparison-based multi-objective algorithm, for the Hausdorff distance, is not much better than the convergence rate of the random search, unless the number of objectives is very moderate, in a framework in which the stronger assumption is that the objectives have conflicts. Our conclusions are (i) the relevance of the number of conflicting objectives (ii) the relevance of random-search-based criterions (iii) the very-hardness of more than 3- objectives optimization (iv) some hints about new cross-over operators. Conference Object Iceland École Polytechnique, Université Paris-Saclay: HAL |
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École Polytechnique, Université Paris-Saclay: HAL |
op_collection_id |
ftepunivpsaclay |
language |
English |
topic |
Multi-Objective Optimization Evolutionary Computation Theory [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA] [SCCO.COMP]Cognitive science/Computer science |
spellingShingle |
Multi-Objective Optimization Evolutionary Computation Theory [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA] [SCCO.COMP]Cognitive science/Computer science Teytaud, Olivier How entropy-theorems can show that approximating high-dim Pareto-fronts is too hard |
topic_facet |
Multi-Objective Optimization Evolutionary Computation Theory [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA] [SCCO.COMP]Cognitive science/Computer science |
description |
It is empirically established that multiobjective evolutionary algorithms do not scale well with the number of conflicting objectives. We here show that the convergence rate of any comparison-based multi-objective algorithm, for the Hausdorff distance, is not much better than the convergence rate of the random search, unless the number of objectives is very moderate, in a framework in which the stronger assumption is that the objectives have conflicts. Our conclusions are (i) the relevance of the number of conflicting objectives (ii) the relevance of random-search-based criterions (iii) the very-hardness of more than 3- objectives optimization (iv) some hints about new cross-over operators. |
author2 |
Algorithmic number theory for cryptology (TANC) Laboratoire d'informatique de l'École polytechnique Palaiseau (LIX) École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
format |
Conference Object |
author |
Teytaud, Olivier |
author_facet |
Teytaud, Olivier |
author_sort |
Teytaud, Olivier |
title |
How entropy-theorems can show that approximating high-dim Pareto-fronts is too hard |
title_short |
How entropy-theorems can show that approximating high-dim Pareto-fronts is too hard |
title_full |
How entropy-theorems can show that approximating high-dim Pareto-fronts is too hard |
title_fullStr |
How entropy-theorems can show that approximating high-dim Pareto-fronts is too hard |
title_full_unstemmed |
How entropy-theorems can show that approximating high-dim Pareto-fronts is too hard |
title_sort |
how entropy-theorems can show that approximating high-dim pareto-fronts is too hard |
publisher |
HAL CCSD |
publishDate |
2006 |
url |
https://hal.science/hal-00113362 https://hal.science/hal-00113362/document https://hal.science/hal-00113362/file/pareto2.pdf |
op_coverage |
Reykjavik, Iceland |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
Bridging the Gap between Theory and Practice - Workshop PPSN-BTP https://hal.science/hal-00113362 Bridging the Gap between Theory and Practice - Workshop PPSN-BTP, 2006, Reykjavik, Iceland |
op_relation |
hal-00113362 https://hal.science/hal-00113362 https://hal.science/hal-00113362/document https://hal.science/hal-00113362/file/pareto2.pdf |
op_rights |
info:eu-repo/semantics/OpenAccess |
_version_ |
1766039087713615872 |