Hybrid Neural Genetic Architecture

2013 ◽  
pp. 245-270
Author(s):  
Emmanuel Buabin
Keyword(s):  
Genetic Algorithm ◽  
Human Nature ◽  
Recommender Systems ◽  
Genetic Architecture ◽  
Network Performance ◽  
Human Genetics ◽  
Systems Design ◽  
Splice Junction ◽  
Fully Integrated ◽  
Active Research

The objective is a neural-based feature selection in intelligent recommender systems. In particular, a hybrid neural genetic architecture is modeled based on human nature, interactions, and behaviour. The main contribution of this chapter is the development of a novel genetic algorithm based on human nature, interactions, and behaviour. The novel genetic algorithm termed “Buabin Algorithm” is fully integrated with a hybrid neural classifier to form a Hybrid Neural Genetic Architecture. The research presents GA in a more attractive manner and opens up the various departments of a GA for active research. Although no scientific experiment is conducted to compare network performance with standard approaches, engaged techniques reveal drastic reductions in genetic operator operations. For illustration purposes, the UCI Molecular Biology (Splice Junction) dataset is used. Overall, “Buabin Algorithm” seeks to integrate human related interactions into genetic algorithms as imitate human genetics in recommender systems design and understand underlying datasets explicitly.

Hybrid Neural Genetic Architecture

2013 ◽  
pp. 761-785
Author(s):  
Emmanuel Buabin
Keyword(s):  
Genetic Algorithm ◽  
Human Nature ◽  
Recommender Systems ◽  
Genetic Architecture ◽  
Network Performance ◽  
Human Genetics ◽  
Systems Design ◽  
Splice Junction ◽  
Fully Integrated ◽  
Active Research

The objective is a neural-based feature selection in intelligent recommender systems. In particular, a hybrid neural genetic architecture is modeled based on human nature, interactions, and behaviour. The main contribution of this chapter is the development of a novel genetic algorithm based on human nature, interactions, and behaviour. The novel genetic algorithm termed “Buabin Algorithm” is fully integrated with a hybrid neural classifier to form a Hybrid Neural Genetic Architecture. The research presents GA in a more attractive manner and opens up the various departments of a GA for active research. Although no scientific experiment is conducted to compare network performance with standard approaches, engaged techniques reveal drastic reductions in genetic operator operations. For illustration purposes, the UCI Molecular Biology (Splice Junction) dataset is used. Overall, “Buabin Algorithm” seeks to integrate human related interactions into genetic algorithms as imitate human genetics in recommender systems design and understand underlying datasets explicitly.

scholarly journals A Genetic Algorithm Approach for Group Recommender System Based on Partial Rankings

2018 ◽  
Vol 29 (1) ◽  
pp. 653-663 ◽  
Author(s):  
Ritu Meena ◽  
Kamal K. Bharadwaj
Keyword(s):  
Genetic Algorithm ◽  
Recommender Systems ◽  
Rank Aggregation ◽  
Partial Ranking ◽  
Novel Approach ◽  
Kendall Tau Distance ◽  
Ultimate Objective ◽  
Aggregation Technique ◽  
The Individual ◽  
Group Recommender

Abstract Many recommender systems frequently make suggestions for group consumable items to the individual users. There has been much work done in group recommender systems (GRSs) with full ranking, but partial ranking (PR) where items are partially ranked still remains a challenge. The ultimate objective of this work is to propose rank aggregation technique for effectively handling the PR problem. Additionally, in real applications, most of the studies have focused on PR without ties (PRWOT). However, the rankings may have ties where some items are placed in the same position, but where some items are partially ranked to be aggregated may not be permutations. In this work, in order to handle problem of PR in GRS for PRWOT and PR with ties (PRWT), we propose a novel approach to GRS based on genetic algorithm (GA) where for PRWOT Spearman foot rule distance and for PRWT Kendall tau distance with bucket order are used as fitness functions. Experimental results are presented that clearly demonstrate that our proposed GRS based on GA for PRWOT (GRS-GA-PRWOT) and PRWT (GRS-GA-PRWT) outperforms well-known baseline GRS techniques.

scholarly journals Recommender Systems for MOOCs: A Systematic Literature Survey (January 1, 2012 – July 12, 2019)

2020 ◽  
Vol 21 (4) ◽  
pp. 255-291
Author(s):  
Asra Khalid ◽  
Karsten Lundqvist ◽  
Anne Yates
Keyword(s):  
Quantitative Analysis ◽  
Literature Review ◽  
Student Performance ◽  
Recommender Systems ◽  
Online Courses ◽  
Systems Design ◽  
Literature Survey ◽  
Massive Open Online Courses ◽  
Future Research ◽  
Massive Open Online

In recent years, massive open online courses (MOOCs) have gained popularity with learners and providers, and thus MOOC providers have started to further enhance the use of MOOCs through recommender systems. This paper is a systematic literature review on the use of recommender systems for MOOCs, examining works published between January 1, 2012 and July 12, 2019 and, to the best of our knowledge, it is the first of its kind. We used Google Scholar, five academic databases (IEEE, ACM, Springer, ScienceDirect, and ERIC) and a reference chaining technique for this research. Through quantitative analysis, we identified the types and trends of research carried out in this field. The research falls into three major categories: (a) the need for recommender systems, (b) proposed recommender systems, and (c) implemented recommender systems. From the literature, we found that research has been conducted in seven areas of MOOCs: courses, threads, peers, learning elements, MOOC provider/teacher recommender, student performance recommender, and others. To date, the research has mostly focused on the implementation of recommender systems, particularly course recommender systems. Areas for future research and implementation include design of practical and scalable online recommender systems, design of a recommender system for MOOC provider and teacher, and usefulness of recommender systems.  

Towards a Hybrid Solver: Integration of a Genetic Algorithm Within “DSIDES”

2002 ◽  
Author(s):  
Shuiwei Xie ◽  
Warren F. Smith
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Linear Programming ◽  
Decision Support ◽  
Hybrid Genetic Algorithm ◽  
Systems Design ◽  
Nonlinear Problems ◽  
The Body ◽  
Hybrid Solver ◽  
Decision Based Design

In contributing to the body of knowledge for decision-based design, the work reported in this paper has involved steps towards building a hybrid genetic algorithm to address systems design. Highlighted is a work in progress at the Australian Defence Force Academy (ADFA). A genetic algorithm (GA) is proposed to deal with discrete aspects of a design model (e.g., allocation of space to function) and a sequential linear programming (SLP) method for the continuous aspects (e.g., sizing). Our historical Decision Based Design (DBD) tool has been the code DSIDES (Decision Support In the Design of Engineering Systems). The original functionality of DSIDES was to solve linear and non-linear goal programming styled problems using linear programming (LP) and sequential (adaptive) linear programming (SLP/ALP). We seek to enhance DSIDES’s solver capability by the addition of genetic algorithms. We will also develop the appropriate tools to deal with the decomposition and synthesis implied. The foundational paradigm for DSIDES, which remains unchanged, is the Decision Support Problem Technique (DSPT). Through introducing genetic algorithms as solvers in DSIDES, the intention is to improve the likelihood of finding the global minimum (for the formulated model) as well as the ability of dealing more effectively with nonlinear problems which have discrete variables, undifferentiable objective functions or undifferentiable constraints. Using some numerical examples and a practical ship design case study, the proposed GA based method is demonstrated to be better in maintaining diversity of populations, preventing premature convergence, compared with other similar GAs. It also has similar effectiveness in finding the solutions as the original ALP DSIDES solver.

scholarly journals Top-N Recommender Systems Using Genetic Algorithm-Based Visual-Clustering Methods

Symmetry ◽  
2016 ◽  
Vol 8 (7) ◽  
pp. 54 ◽  
Author(s):  
Ukrit Marung ◽  
Nipon Theera-Umpon ◽  
Sansanee Auephanwiriyakul
Keyword(s):  
Genetic Algorithm ◽  
Recommender Systems ◽  
Clustering Methods ◽  
Visual Clustering

Genetic Algorithm Based Parallel Matrix Factorization for Recommender Systems

2016 ◽  
Author(s):  
B. Vaibhav Srivastava ◽  
Shashikant Sharma ◽  
Deepanwita Datta ◽  
Guduri Sriram ◽  
Saket Jambhulkar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Recommender Systems ◽  
Matrix Factorization

Semantics-aware content-based recommender systems: Design and architecture guidelines

2017 ◽  
Vol 254 ◽  
pp. 79-85 ◽  
Author(s):  
Ludovico Boratto ◽  
Salvatore Carta ◽  
Gianni Fenu ◽  
Roberto Saia
Keyword(s):  
Recommender Systems ◽  
Systems Design
Sign in / Sign up

Export Citation Format

Share Document