Re: [bug?] changed behaviour of column view

[Eric S Fraga <e.fraga@ucl.ac.uk>]

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Hi Nicholas,

On Tuesday, 11 May 2021 at 09:32, Nicholas Savage wrote:
> When I activate column view on Headline, it applies the columns on all
> of the subheadings. Are you not seeing the same thing, or is your use
> case different? Do you have some sort of minimal file you could
> provide to show the bug?

If point is on the first (top-level) headline, I only get the column
view for that headline and not the subheadings.

Attached is a small(-ish) file along with a screenshot of what I get.

The org file consists of the first three papers in a bibliography I have
(which has hundreds of entries).

If you are seeing something different, maybe I have some setting that is
causing problems although I cannot imagine what that would be. Advice
would be welcome!

Thank you,
eric

-- 
: Eric S Fraga via Emacs 28.0.50, Org release_9.4.5-534-g8f03cd

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#+columns: %30CUSTOM_ID %60TITLE %40AUTHOR
* All papers
** The {GNU} version of {T}he {C}ollaborative {I}nternational {D}ictionary of {E}nglish :ATTACH:
:PROPERTIES:
:TITLE:    The {GNU} version of {T}he {C}ollaborative {I}nternational {D}ictionary of {E}nglish
:BTYPE:    misc
:CUSTOM_ID: GCIDE
:HOWPUBLISHED: Published on the Internet
:NOTE:     \url{ftp://ftp.gnu.org/gnu/gcide/}
:Attachments: Powell-UnifiedFrameworkStochasticOptimization_Jan292018.pdf
:ID:         f29925d3-8466-4dd7-9843-31cd66ef885a
:END:
** {An Evolutionary Algorithm Based on Minkowski Distance for Many-Objective Optimization}
:PROPERTIES:
:TITLE:      {An Evolutionary Algorithm Based on Minkowski Distance for Many-Objective Optimization}
:BTYPE:      article
:CUSTOM_ID:  ISI:000476811000012
:AUTHOR:     Xu, Hang and Zeng, Wenhua and Zeng, Xiangxiang and Yen, Gary G.
:JOURNAL:    {IEEE TRANSACTIONS ON CYBERNETICS}
:YEAR:       {2019}
:VOLUME:     {49}
:NUMBER:     {11}
:PAGES:      {3968-3979}
:MONTH:      {NOV}
:ABSTRACT:   {The existing multiobjective evolutionary algorithms (EAs) based on nondominated sorting may encounter serious difficulties in tackling many-objective optimization problems (MaOPs), because the number of nondominated solutions increases exponentially with the number of objectives, leading to a severe loss of selection pressure. To address this problem, some existing many-objective EAs (MaOEAs) adopt Euclidean or Manhattan distance to estimate the convergence of each solution during the environmental selection process. Nevertheless, either Euclidean or Manhattan distance is a special case of Minkowski distance with the order P = 2 or P = 1, respectively. Thus, it is natural to adopt Minkowski distance for convergence estimation, in order to cover various types of Pareto fronts (PFs) with different concavity-convexity degrees. In this paper, a Minkowski distance-based EA is proposed to solve MaOPs. In the proposed algorithm, first, the concavity-convexity degree of the approximate PF, denoted by the value of P, is dynamically estimated. Subsequently, the Minkowski distance of order P is used to estimate the convergence of each solution. Finally, the optimal solutions are selected by a comprehensive method, based on both convergence and diversity. In the experiments, the proposed algorithm is compared with five state-of-the-art MaOEAs on some widely used benchmark problems. Moreover, the modified versions for two compared algorithms, integrated with the proposed P-estimation method and the Minkowski distance, are also designed and analyzed. Empirical results show that the proposed algorithm is very competitive against other MaOEAs for solving MaOPs, and two modified compared algorithms are generally more effective than their predecessors.}
:PUBLISHER:  {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC}
:ADDRESS:    {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA}
:LANGUAGE:   {English}
:AFFILIATION: {Zeng, XX (Reprint Author), Xiamen Univ, Sch Informat Sci \& Engn, Xiamen 361005, Fujian, Peoples R China. Yen, GG (Reprint Author), Oklahoma State Univ, Sch Elect \& Comp Engn, Stillwater, OK 74078 USA. Xu, Hang, Putian Univ, Sch Informat Engn, Putian 351100, Peoples R China. Xu, Hang; Zeng, Wenhua, Xiamen Univ, Sch Software, Xiamen 361005, Fujian, Peoples R China. Zeng, Xiangxiang, Xiamen Univ, Sch Informat Sci \& Engn, Xiamen 361005, Fujian, Peoples R China. Yen, Gary G., Oklahoma State Univ, Sch Elect \& Comp Engn, Stillwater, OK 74078 USA.}
:DOI:        {10.1109/TCYB.2018.2856208}
:ISSN:       {2168-2267}
:EISSN:      {2168-2275}
:KEYWORDS:   {Concavity-convexity degree; convergence estimation; evolutionary algorithm (EA); many-objective optimization; Minkowski distance}
:KEYWORDS-PLUS: {NONDOMINATED SORTING APPROACH; BOUNDARY INTERSECTION; SELECTION; PERFORMANCE; MOEA/D; CONVERGENCE; DIVERSITY; DOMINANCE}
:RESEARCH-AREAS: {Automation \& Control Systems; Computer Science}
:WEB-OF-SCIENCE-CATEGORIES: {Automation \& Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics}
:AUTHOR-EMAIL: {hangxu520@hotmail.com whzeng@xmu.edu.cn xzeng@xmu.edu.cn gyen@okstate.edu}
:ORCID-NUMBERS: {Xu, Hang/0000-0002-3376-9487 Yen, Gary/0000-0001-8851-5348}
:FUNDING-ACKNOWLEDGEMENT: {National Natural Science Foundation of ChinaNational Natural Science Foundation of China {[}61472333, 61772441, 61472335, 61272152, 41476118]; Natural Science Foundation of the Higher Education Institutions of Fujian Province {[}JZ160400]; President Fund of Xiamen University {[}20720170054]; Juan de la Cierva Position {[}IJCI-2015-26991]; MINECO AEI/FEDER, EU {[}TIN2016-81079-R]; Madrid Government {[}B2017/BMD-3691]; InGEMICS-CM (FSE/FEDER, EU)}
:FUNDING-TEXT: {This work was supported in part by the National Natural Science Foundation of China under Grant 61472333, Grant 61772441, Grant 61472335, Grant 61272152, and Grant 41476118, in part by the Natural Science Foundation of the Higher Education Institutions of Fujian Province under Grant JZ160400, and in part by the President Fund of Xiamen University under Grant 20720170054. The work of X. Zeng was supported in part by the Juan de la Cierva Position under Grant IJCI-2015-26991, in part by the MINECO AEI/FEDER, EU under Spanish TIN2016-81079-R, in part by the Madrid Government under Grant B2017/BMD-3691, and in part by the InGEMICS-CM (FSE/FEDER, EU). This paper was recommended by Associate Editor M. Zhang. (Corresponding authors: Xiangxiang Zeng; Gary G. Yen.)}
:NUMBER-OF-CITED-REFERENCES: {79}
:TIMES-CITED: {2}
:USAGE-COUNT-LAST-180-DAYS: {18}
:USAGE-COUNT-SINCE-2013: {22}
:JOURNAL-ISO: {IEEE T. Cybern.}
:DOC-DELIVERY-NUMBER: {IK7YY}
:UNIQUE-ID:  {ISI:000476811000012}
:DA:         {2019-09-29}
:END:
** {Evolutionary Many-Objective Algorithm Using Decomposition-Based Dominance Relationship}
:PROPERTIES:
:TITLE:      {Evolutionary Many-Objective Algorithm Using Decomposition-Based Dominance Relationship}
:BTYPE:      article
:CUSTOM_ID:  ISI:000485687200007
:AUTHOR:     Chen, Lei and Liu, Hai-Lin and Tan, Kay Chen and Cheung, Yiu-Ming and Wang, Yuping
:JOURNAL:    {IEEE TRANSACTIONS ON CYBERNETICS}
:YEAR:       {2019}
:VOLUME:     {49}
:NUMBER:     {12}
:PAGES:      {4129-4139}
:MONTH:      {DEC}
:ABSTRACT:   {Decomposition-based evolutionary algorithms have shown great potential in many-objective optimization. However, the lack of theoretical studies on decomposition methods has hindered their further development and application. In this paper, we first theoretically prove that weight sum, Tchebycheff, and penalty boundary intersection decomposition methods are essentially interconnected. Inspired by this, we further show that highly customized dominance relationship can be derived from decomposition for any given decomposition vector. A new evolutionary algorithm is then proposed by applying the customized dominance relationship with adaptive strategy to each subpopulation of multiobjective to multiobjective framework. Experiments are conducted to compare the proposed algorithm with five state-of-the-art decomposition-based evolutionary algorithms on a set of well-known scaled many-objective test problems with 5 to 15 objectives. Simulation results have shown that the proposed algorithm can make better use of the decomposition vectors to achieve better performance. Further investigations on unsealed many-objective test problems verify the robust and generality of the proposed algorithm.}
:PUBLISHER:  {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC}
:ADDRESS:    {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA}
:LANGUAGE:   {English}
:AFFILIATION: {Liu, HL (Reprint Author), Guangdong Univ Technol, Guangzhou 510006, Guangdong, Peoples R China. Chen, Lei; Liu, Hai-Lin, Guangdong Univ Technol, Guangzhou 510006, Guangdong, Peoples R China. Tan, Kay Chen, City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China. Cheung, Yiu-Ming, Hong Kong Baptist Univ, Dept Comp Sci, Hong Kong, Peoples R China. Wang, Yuping, Xidian Univ, Dept Comp Sci, Xian 710071, Shaanxi, Peoples R China.}
:DOI:        {10.1109/TCYB.2018.2859171}
:ISSN:       {2168-2267}
:EISSN:      {2168-2275}
:KEYWORDS:   {Dominance relationship; evolutionary algorithm; many-objective; multiobjective to multiobjective (M2M) decomposition}
:RESEARCH-AREAS: {Automation \& Control Systems; Computer Science}
:WEB-OF-SCIENCE-CATEGORIES: {Automation \& Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics}
:AUTHOR-EMAIL: {chenaction@126.com hlliu@gdut.edu.cn kaytan@cityu.edu.hk ymc@comp.hkbu.edu.hk ywang@xidian.edu.cn}
:RESEARCHERID-NUMBERS: {Cheung, Yiu-ming/E-2050-2015 }
:ORCID-NUMBERS: {Cheung, Yiu-ming/0000-0001-7629-4648 Wang, Yuping/0000-0001-6868-0004 Liu, Hai-Lin/0000-0003-2276-1938}
:FUNDING-ACKNOWLEDGEMENT: {National Natural Science Foundation of ChinaNational Natural Science Foundation of China {[}61673121, 61672444]; Projects of Science and Technology of Guangzhou {[}201804010352]; City University of Hong Kong Research Fund {[}7200543]; China Scholarship CouncilChina Scholarship Council}
:FUNDING-TEXT: {This work was supported in part by the National Natural Science Foundation of China under Grant 61673121 and Grant 61672444, in part by the Projects of Science and Technology of Guangzhou under Grant 201804010352, in part by the City University of Hong Kong Research Fund under Grant 7200543, and in part by the China Scholarship Council.}
:NUMBER-OF-CITED-REFERENCES: {41}
:TIMES-CITED: {0}
:USAGE-COUNT-LAST-180-DAYS: {0}
:USAGE-COUNT-SINCE-2013: {2}
:JOURNAL-ISO: {IEEE T. Cybern.}
:DOC-DELIVERY-NUMBER: {IX4WU}
:UNIQUE-ID:  {ISI:000485687200007}
:DA:         {2019-09-29}
:END: