Model Genetic Rules Based Systems for Evaluation of Projects

Jesus Silva; John Freddy Escobar Gomez; Ernesto Steffens Sanabria; Hugo Hernandez Palma; Midori Ikeda; Jorge Linares; Nohora Mercado

doi:10.1016/j.procs.2020.03.069

Model Genetic Rules Based Systems for Evaluation of Projects

Jesus Silva
, John Freddy Escobar Gomez
, Ernesto Steffens Sanabria
, Hugo Hernandez Palma
, Midori Ikeda
, Jorge Linares
, Nohora Mercado

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

The process of project evaluation is of vital importance for decision-making in organizations. In the particular case of IT projects, the historical average of successful projects is 30.7%, while renegotiated projects are 47.3% and cancelled projects are 22% [1]. These figures mean that huge budgets are affected every year by errors in planning or control and monitoring of projects, with an economic and social impact. The objective of this research is to evaluate the MCGEP evolutionary algorithm in different versions databases with information on the evaluation of IT projects. The aim is to determine the possibility of applying an evolutionary algorithm that uses programming of genetic expressions as opposed to others of greater use.

Original language	English
Pages (from-to)	390-395
Number of pages	6
Journal	Procedia Computer Science
Volume	170
DOIs	https://doi.org/10.1016/j.procs.2020.03.069
State	Published - 2020
Externally published	Yes
Event	11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops - Warsaw, Poland Duration: 6 Apr 2020 → 9 Apr 2020

Keywords

Gene Expression Programming
Genetic Algorithms
MCGEP Algorithm
Project Evaluation
Rules learning

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10.1016/j.procs.2020.03.069

Cite this

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title = "Model Genetic Rules Based Systems for Evaluation of Projects",

abstract = "The process of project evaluation is of vital importance for decision-making in organizations. In the particular case of IT projects, the historical average of successful projects is 30.7\%, while renegotiated projects are 47.3\% and cancelled projects are 22\% [1]. These figures mean that huge budgets are affected every year by errors in planning or control and monitoring of projects, with an economic and social impact. The objective of this research is to evaluate the MCGEP evolutionary algorithm in different versions databases with information on the evaluation of IT projects. The aim is to determine the possibility of applying an evolutionary algorithm that uses programming of genetic expressions as opposed to others of greater use.",

keywords = "Gene Expression Programming, Genetic Algorithms, MCGEP Algorithm, Project Evaluation, Rules learning, Genetic Algorithms, Gene Expression Programming, MCGEP Algorithm, Project Evaluation, Rules learning",

author = "Jesus Silva and \{Escobar Gomez\}, \{John Freddy\} and Sanabria, \{Ernesto Steffens\} and Palma, \{Hugo Hernandez\} and Midori Ikeda and Jorge Linares and Nohora Mercado",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors. Published by Elsevier B.V.All rights reserved.; 11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops ; Conference date: 06-04-2020 Through 09-04-2020",

year = "2020",

doi = "10.1016/j.procs.2020.03.069",

language = "Ingl{\'e}s",

volume = "170",

pages = "390--395",

journal = "Procedia Computer Science",

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TY - JOUR

T1 - Model Genetic Rules Based Systems for Evaluation of Projects

AU - Silva, Jesus

AU - Escobar Gomez, John Freddy

AU - Sanabria, Ernesto Steffens

AU - Palma, Hugo Hernandez

AU - Ikeda, Midori

AU - Linares, Jorge

AU - Mercado, Nohora

PY - 2020

Y1 - 2020

N2 - The process of project evaluation is of vital importance for decision-making in organizations. In the particular case of IT projects, the historical average of successful projects is 30.7%, while renegotiated projects are 47.3% and cancelled projects are 22% [1]. These figures mean that huge budgets are affected every year by errors in planning or control and monitoring of projects, with an economic and social impact. The objective of this research is to evaluate the MCGEP evolutionary algorithm in different versions databases with information on the evaluation of IT projects. The aim is to determine the possibility of applying an evolutionary algorithm that uses programming of genetic expressions as opposed to others of greater use.

AB - The process of project evaluation is of vital importance for decision-making in organizations. In the particular case of IT projects, the historical average of successful projects is 30.7%, while renegotiated projects are 47.3% and cancelled projects are 22% [1]. These figures mean that huge budgets are affected every year by errors in planning or control and monitoring of projects, with an economic and social impact. The objective of this research is to evaluate the MCGEP evolutionary algorithm in different versions databases with information on the evaluation of IT projects. The aim is to determine the possibility of applying an evolutionary algorithm that uses programming of genetic expressions as opposed to others of greater use.

KW - Gene Expression Programming

KW - Genetic Algorithms

KW - MCGEP Algorithm

KW - Project Evaluation

KW - Rules learning

KW - Genetic Algorithms

KW - Gene Expression Programming

KW - MCGEP Algorithm

KW - Project Evaluation

KW - Rules learning

UR - https://www.scopus.com/pages/publications/85085562435

U2 - 10.1016/j.procs.2020.03.069

DO - 10.1016/j.procs.2020.03.069

M3 - Artículo de la conferencia

AN - SCOPUS:85085562435

SN - 1877-0509

VL - 170

SP - 390

EP - 395

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 11th International Conference on Ambient Systems, Networks and Technologies, ANT 2020 / 3rd International Conference on Emerging Data and Industry 4.0, EDI40 2020 / Affiliated Workshops

Y2 - 6 April 2020 through 9 April 2020

ER -

Model Genetic Rules Based Systems for Evaluation of Projects

Abstract

Keywords

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