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Documents authored by Yao, Xin

Document
DOI: 10.4230/DagRep.9.10.61
Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431)

Authors: Carola Doerr, Carlos M. Fonseca, Tobias Friedrich, and Xin Yao

Published in: Dagstuhl Reports, Volume 9, Issue 10 (2020)

Abstract
This report documents the activities of Dagstuhl Seminar 19431 on Theory of Randomized Optimization Heuristics. 46 researchers from Europe, Australia, Asia, and North America have come together to discuss ongoing research. This tenth edition of the seminar series had three focus topics: (1) relation between optimal control and heuristic optimization, (2) benchmarking optimization heuristics, and (3) the interfaces between continuous and discrete optimization. Several breakout sessions have provided ample opportunity to brainstorm on recent developments in the research landscape, to discuss and solve open problems, and to kick-start new research initiatives.
Cite as

Carola Doerr, Carlos M. Fonseca, Tobias Friedrich, and Xin Yao. Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431). In Dagstuhl Reports, Volume 9, Issue 10, pp. 61-94, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{doerr_et_al:DagRep.9.10.61,
  author =	{Doerr, Carola and Fonseca, Carlos M. and Friedrich, Tobias and Yao, Xin},
  title =	{{Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431)}},
  pages =	{61--94},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{9},
  number =	{10},
  editor =	{Doerr, Carola and Fonseca, Carlos M. and Friedrich, Tobias and Yao, Xin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.9.10.61},
  URN =		{urn:nbn:de:0030-drops-118567},
  doi =		{10.4230/DagRep.9.10.61},
  annote =	{Keywords: algorithms and complexity, evolutionary algorithms, machine learning, optimization, soft computing}
}
Document
DOI: 10.4230/DagRep.7.5.22
Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191)

Authors: Carola Doerr, Christian Igel, Lothar Thiele, and Xin Yao

Published in: Dagstuhl Reports, Volume 7, Issue 5 (2018)

Abstract
This report summarizes the talks, breakout sessions, and discussions at the Dagstuhl Seminar 17191 on "Theory of Randomized Optimization Heuristics", held during the week from May 08 until May 12, 2017, in Schloss Dagstuhl -- Leibniz Center for Informatics. The meeting is the successor of the "Theory of Evolutionary Algorithm" seminar series, where the change in the title reflects the development of the research field toward a broader range of heuristics. The seminar has hosted 40 researchers from 15 countries. Topics that have been intensively discussed at the seminar include population-based heuristics, constrained optimization, non-static parameter choices as well as connections to research in machine learning.
Cite as

Carola Doerr, Christian Igel, Lothar Thiele, and Xin Yao. Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191). In Dagstuhl Reports, Volume 7, Issue 5, pp. 22-55, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@Article{doerr_et_al:DagRep.7.5.22,
  author =	{Doerr, Carola and Igel, Christian and Thiele, Lothar and Yao, Xin},
  title =	{{Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191)}},
  pages =	{22--55},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2017},
  volume =	{7},
  number =	{5},
  editor =	{Doerr, Carola and Igel, Christian and Thiele, Lothar and Yao, Xin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.5.22},
  URN =		{urn:nbn:de:0030-drops-82797},
  doi =		{10.4230/DagRep.7.5.22},
  annote =	{Keywords: algorithms and complexity, evolutionary algorithms, machine learning, optimization, soft computing}
}
Document
DOI: 10.4230/DagSemProc.08051.3
A Comparison of GAs Penalizing Infeasible Solutions and Repairing Infeasible Solutions on the 0-1 Knapsack Problem

Authors: Jun He, Yuren Zhou, and Xin Yao

Published in: Dagstuhl Seminar Proceedings, Volume 8051, Theory of Evolutionary Algorithms (2008)

Abstract
Constraints exist in almost every optimization problem. Different constraint handling techniques have been incorporated with genetic algorithms (GAs), however most of current studies are based on computer experiments. An example is Michalewicz's comparison among GAs using different constraint handling techniques on the 0-1 knapsack problem. The following phenomena are observed in experiments: 1) the penalty method needs more generations to find a feasible solution to the restrictive capacity knapsack than the repair method; 2) the penalty method can find better solutions to the average capacity knapsack. Such observations need a theoretical explanation. This paper aims at providing a theoretical analysis of Michalewicz's experiments. The main result of the paper is that GAs using the repair method are more efficient than GAs using the penalty method on both restrictive capacity and average capacity knapsack problems. This result of the average capacity is a little different from Michalewicz's experimental results. So a supplemental experiment is implemented to support the theoretical claim. The results confirm the general principle pointed out by Coello: a better constraint-handling approach should tend to exploit specific domain knowledge.
Cite as

Jun He, Yuren Zhou, and Xin Yao. A Comparison of GAs Penalizing Infeasible Solutions and Repairing Infeasible Solutions on the 0-1 Knapsack Problem. In Theory of Evolutionary Algorithms. Dagstuhl Seminar Proceedings, Volume 8051, pp. 1-39, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)

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@InProceedings{he_et_al:DagSemProc.08051.3,
  author =	{He, Jun and Zhou, Yuren and Yao, Xin},
  title =	{{A Comparison of GAs Penalizing Infeasible Solutions and Repairing Infeasible Solutions on the 0-1 Knapsack Problem}},
  booktitle =	{Theory of Evolutionary Algorithms},
  pages =	{1--39},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2008},
  volume =	{8051},
  editor =	{Dirk V. Arnold and Anne Auger and Jonathan E. Rowe and Carsten Witt},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08051.3},
  URN =		{urn:nbn:de:0030-drops-14822},
  doi =		{10.4230/DagSemProc.08051.3},
  annote =	{Keywords: Genetic Algorithms, Constrained Optimization, Knapsack Problem, Computation Time, Performance Analysis}
}
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