Methods and systems for multi-participant interactive evolutionary computing
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G06F-015/18
G06N-003/00
G06N-003/12
출원번호
US-0922777
(2004-08-20)
등록번호
US-7356518
(2008-04-08)
발명자
/ 주소
Bonabeau,Eric
Funes,Pablo
출원인 / 주소
Icosystem Corporation
대리인 / 주소
Foley Hoag LLP
인용정보
피인용 횟수 :
13인용 특허 :
65
초록▼
Disclosed are methods, systems, and processor program products that include executing an optimization scheme to obtain a first solution set, presenting the first solution set to at least two users, receiving rankings of the first solution set from the at least two users, aggregating the rankings, an
Disclosed are methods, systems, and processor program products that include executing an optimization scheme to obtain a first solution set, presenting the first solution set to at least two users, receiving rankings of the first solution set from the at least two users, aggregating the rankings, and, generating a second solution set based on the aggregated rankings. The optimization scheme can include a genetic algorithm. In embodiments, at least a part of the first solution set can be presented to the users based on the parts of the solution set associated with the user (e.g., user's knowledge).
대표청구항▼
What is claimed is: 1. A computer-readable medium having computer-readable instructions stored thereon which when executed in a computer system having at least one user interface including at lest one output device and at least one input device, instruct the computer system to perform a method, com
What is claimed is: 1. A computer-readable medium having computer-readable instructions stored thereon which when executed in a computer system having at least one user interface including at lest one output device and at least one input device, instruct the computer system to perform a method, comprising: a) causing at least a part of a solution set to be presented through at least one of the at least one output devices to at least two first-level users, b) receiving through at least one of the at least one input devices, from at least one of the at least two first-level users, a ranking of the at least a part of the solution set presented to that user, c) if rankings of at least a part of the solution set were received from a plurality of first-level users, aggregating the rankings and generating an updated solution set based on the aggregated rankings, d) if rankings of at least a part the solution set were received from only one first-level user, generating an updated solution set based on the ranking by the said user, e) causing the updated solution set to be presented through at least one of the at least one output devices to at least one second-level user, f) receiving through at least one of the at least one input devices, from at least one of the at least one second-level users, a ranking of the updated solution set, g) if rankings of the updated solution set were received from a plurality of second-level users, aggregating the rankings, and generating a further updated solution set based on the aggregated rankings, h) if rankings of the updated solution set were received from only one second-level user, generating a further updated solution set based on the ranking by the said user, i) determining if a stopping criterion has been satisfied, j) if the said stopping criterion has not been satisfied, repeating steps a) through i) based upon the further updated solution set, and k) if the said stopping criterion has been satisfied, causing at least a portion of the further updated solution set to be presented to at least one user through at least one of the at least one output devices. 2. A computer-readable medium according to claim 1, where generating an updated solution set includes generating the updated solution set using an optimization scheme. 3. A computer-readable medium according to claim 2, where the optimization scheme comprises a genetic algorithm. 4. A computer-readable medium according to claim 3, where the genetic algorithm is a single generation genetic algorithm. 5. A computer-readable medium according to claim 3, where the genetic algorithm is a multiple generation genetic algorithm. 6. A computer-readable medium according to claim 1, where causing at least a part of a solution set to be presented through at least one of the at least one output devices includes: determining, for each first-level user, which at least a part of the solution set is associated with the said user, and, causing to be presented to each user through at least one of the at least one output devices the at least a part of the first solution set associated with the said user. 7. A computer-readable medium according to claim 6, where determining which at least a part of the solution set is associated with the said user includes determining based on at least one of a knowledge of the said user, an involvement of the said user, and an experience level of the said user. 8. A computer-readable medium according to claim 1, where causing at least a part of a solution set to be presented through at least one of the at least one output devices includes causing to be presented at least one of: in parallel and in series. 9. A computer-readable medium according to claim 1, where receiving rankings through at least one of the at least one input devices includes receiving rankings within a time period. 10. A computer-readable medium according to claim 9, where receiving rankings within a time period includes: determining that a time period expired, and, assigning a ranking based on a prior ranking received from a user. 11. A computer-readable medium according to claim 9, where receiving rankings within a time period includes: determining that a time period expired, and, assigning a ranking based on a default ranking. 12. A computer-readable medium according to claim 1, where aggregating the rankings includes aggregating based on weightings assigned to the plurality of users from whom the rankings were received. 13. A computer-readable medium according to claim 2, further comprising determining whether at least one exit criterion is satisfied before ceasing to execute the optimization scheme. 14. A computer-readable medium according to claim 2, further comprising adjusting at least a part of the optimization scheme based upon input from at least one second-level user. 15. A computer-readable medium according to claim 14, where adjusting at least part of the optimization scheme includes modifying parameters of the optimization scheme. 16. A computer-readable medium according to claim 15, where modifying parameters of the optimization scheme includes modifying parameters of a genetic algorithm.
연구과제 타임라인
LOADING...
LOADING...
LOADING...
LOADING...
LOADING...
이 특허에 인용된 특허 (65)
Wavish Peter R.,GB2 ; Connah David M.,GB2, Adaptive process modelling and control.
Paulo S. Tubel ; Lynn B. Hales ; Randy A. Ynchausti ; Donald G. Foot, Jr., Application of adaptive object-oriented optimization software to an automatic optimization oilfield hydrocarbon production management system.
Merat Francis L. (University Heights OH) Roumina Kavous (Westlake OH) Ruegsegger Steven M. (Centerville OH) Delvalle Robert B. (Cleveland Heights OH), Automated process planning for quality control inspection.
Choi, Lawrence J.; Kuenne, Christopher B.; Holstein, II, Kurt E., Computer-assisted systems and methods for determining effectiveness of survey question.
Jeffrey J. Garside ; Stephen Monfre ; Barry C. Elliott ; Timothy L. Ruchti ; Glenn Aaron Kees ; Frank S. Grochocki, Fiber optic illumination and detection patterns, shapes, and locations for use in spectroscopic analysis.
Shackleford J. Barry,JPX ; Okushi Etsuko,JPX ; Yasuda Mitsuhiro,JPX ; Iwamoto Takashi,JPX, Genetic algorithm machine and its production method, and method for executing a genetic algorithm.
McCann Paul H. ; Alose Gary L. ; Chavez Javier E. ; Dawson Scott M. ; Brayton Robert S. ; Hiles Paul E., Method and apparatus for an incremental editor technology.
Choi, Lawrence J.; Kuenne, Christopher B.; Holstein, II, Kurt E.; Cross, Henry Andrew; Tang, George; Bansal, Chetna; Whitney, Jason R.; Babbitt, Joshua D., Method and system for clustering optimization and applications.
Koza John R. (25372 La Rena La. Los Altos Hills CA 94022), Non-linear genetic algorithms for solving problems by finding a fit composition of functions.
Ulyanov, Sergei V.; Panfilov, Sergei; Takahashi, Kazuki, System and method for nonlinear dynamic control based on soft computing with discrete constraints.
Elad Joseph B. (Claymont DE) Johnson Apperson H. (Wilmington DE) Kramer Laurence A. (North East MD) Kirk Jeffrey C. (Newtown Square PA) Philips Irene H. (New Castle DE) Zickus Susan M. (Wilmington DE, System and method for representing and solving numeric and symbolic problems.
Elad Joseph B. (Claymont DE) Johnson Apperson H. (Wilmington DE) Kramer Laurence A. (North East MD) Kirk Jeffrey C. (Newtown Square PA) Philips Irene H. (New Castle DE) Zickus Susan M. (Wilmington DE, System and method for representing and solving numeric and symbolic problems.
Funes, Pablo; Popovici, Elena; Gaudiano, Paolo; Buchsbaum, Daphna; Garagic, Denis; Ecemis, M. Ihsan; Bingham, Chris; Bonabeau, Eric, Method and system for fast, generic, online and offline, multi-source text analysis and visualization.
Bonabeau, Eric; Anderson, Carl; Scott, John M.; Budynek, Julien; Malinchik, Sergey, Methods and systems for applying genetic operators to determine system conditions.
Bonabeau, Eric; Anderson, Carl; Scott, John M.; Budynek, Julien; Malinchik, Sergey, Methods and systems for applying genetic operators to determine system conditions.
Bonabeau, Eric; Anderson, Carl; Scott, John M.; Budynek, Julien; Malinchik, Sergey, Methods and systems for applying genetic operators to determine systems conditions.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.