scholarly journals GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks

Author(s):  
Carlos Rojas-Guzmán ◽  
Mark A. Kramer
Author(s):  
Said Tkatek ◽  
Saadia Bahti ◽  
Otman Abdoun ◽  
Jaafar Abouchabaka

<p>The human resources (HR) manager needs effective tools to be able to move away from traditional recruitment processes to make the good decision to select the good candidates for the good posts. To do this, we deliver an intelligent recruitment decision-making method for HR, incorporating a recruitment model based on the multipack model known as the NP-hard model. The system, which is a decision support tool, often integrates a genetic approach that operates alternately in parallel and sequentially. This approach will provide the best recruiting solution to allow HR managers to make the right decision to ensure the best possible compatibility with the desired objectives. Operationally, this system can also predict the altered choice of parallel genetic algorithm (PGA) or sequential genetic algorithm (SeqGA) depending on the size of the instance and constraints of the recruiting posts to produce the quality solution in a reduced CPU time for recruiting decision-making. The results obtained in various tests confirm the performance of this intelligent system which can be used as a decision support tool for intelligently optimized recruitment.</p>


Author(s):  
Witold Abramowicz ◽  
Marek Nowak ◽  
Joanna Sztykiel

The main purpose of this article is to discuss applicability of Bayesian belief networks (BBN) within the procedures of working-capital credit scoring conducted in commercial banks. A brief description of Bayesian formulation of causal dependence and its strength is given. Inferential and diagnostic features of BBN are illustrated using sample structure. As an example we present and compare results of estimating a credit risk using two techniques: traditional credit-scoring system and BBN structure.


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