Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Konstantinos Liagkouras"'
Publikováno v:
European Journal of Operational Research. 292:1019-1036
The fundamental unit of each evolutionary algorithm is the individual. Each individual represents a potential solution to the problem at hand. Despite the importance of individual solution for multi-objective algorithms’ performance the majority of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d121974c7f5fd05b0a46943d518da958
https://doi.org/10.1016/j.ejor.2020.11.028
https://doi.org/10.1016/j.ejor.2020.11.028
Publikováno v:
Annals of Operations Research. 316:1493-1518
Over the last years, more and more companies face increased pressure by the public to provide information on how they perform on environmental, social and governance (ESG) issues. However, so far a very small number of studies have investigated optim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b77d62895e16988c28a514a71c8c246f
https://doi.org/10.1007/s10479-020-03554-3
https://doi.org/10.1007/s10479-020-03554-3
Autor:
Konstantinos Liagkouras
Publikováno v:
Knowledge-Based Systems. 163:186-203
The existing evolutionary algorithm techniques have limited capabilities in solving large-scale combinatorial problems due to their large search space, making impractical the examination of big real-world instances. In this paper, we address this iss
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c45d5f8b68bcbc0a7dba0c93dfb31ac
https://doi.org/10.1016/j.knosys.2018.08.025
https://doi.org/10.1016/j.knosys.2018.08.025
Publikováno v:
Learning and Analytics in Intelligent Systems ISBN: 9783030497231
Support Vector Machine (SVM) is a well established technique within machine learning. Over the last years, Support Vector Machines have been used across a wide range of applications. In this paper, we investigate stock prices forecasting by using a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9532c604b79b272631a8f205231f1a48
https://doi.org/10.1007/978-3-030-49724-8_11
https://doi.org/10.1007/978-3-030-49724-8_11
Publikováno v:
Annals of Operations Research. 272:119-137
This paper examines the incorporation of useful information extracted from the evolutionary process, in order to improve algorithm performance. In order to achieve this objective, we introduce an efficient method of extracting and utilizing valuable
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cfbce42edac12d117345f66d585b7e3b
https://doi.org/10.1007/s10479-018-2876-1
https://doi.org/10.1007/s10479-018-2876-1
Publikováno v:
Journal of the Operational Research Society. 69:1609-1627
The incorporation of additional constraints to the basic mean–variance (MV) model adds realism to the model, but simultaneously makes the problem difficult to be solved with exact approaches. In this paper we address the challenges that have arisen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5dbf7f662d1e537cf591a850528d2f05
https://doi.org/10.1057/s41274-017-0209-4
https://doi.org/10.1057/s41274-017-0209-4
Publikováno v:
Journal of the Operational Research Society. 69:416-438
This article examines the effect of different configuration issues of the Multiobjective Evolutionary Algorithms on the efficient frontier formulation for the constrained portfolio optimization problem. We present the most popular techniques for deal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eba82f7f234c76e8ba1ee1ece4d20ecb
https://doi.org/10.1057/jors.2016.38
https://doi.org/10.1057/jors.2016.38
Publikováno v:
Annals of Operations Research. 267:281-319
This paper proposes a novel multiobjective evolutionary Algorithm (MOEA) for the solution of the cardinality constrained portfolio optimization problem (CCPOP). The proposed algorithm introduces an efficient encoding scheme specially designed for dea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59cb310207ac78bb991c2ab0c34ee9dc
https://doi.org/10.1007/s10479-016-2377-z
https://doi.org/10.1007/s10479-016-2377-z
Publikováno v:
Journal of Experimental & Theoretical Artificial Intelligence. 29:91-131
Multi-objective evolutionary algorithms (MOEAs) are currently a dynamic field of research that has attracted considerable attention. Mutation operators have been utilized by MOEAs as variation mechanisms. In particular, polynomial mutation (PLM) is o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::46008be77d69c7e7a98e376c55d2f86f
https://doi.org/10.1080/0952813x.2015.1132260
https://doi.org/10.1080/0952813x.2015.1132260
Publikováno v:
Soft Computing. 21:721-751
Evolutionary algorithms for multiobjective problems utilize three types of operations for progressing toward the higher fitness regions of the search space. Each type of operator contributes in a different way toward the achievement of the common goa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f3c7bb2b7b96c8047f3856bf01ba070c
https://doi.org/10.1007/s00500-015-1810-6
https://doi.org/10.1007/s00500-015-1810-6