scholarly journals Genetic Algorithms for Noisy Fitness Functions ― Applications, Requirements and Algorithms

2001 ◽  
Vol 2001 (0) ◽  
pp. 137-142 ◽  
Author(s):  
Hajime Kita
Keyword(s):  
Genetic Algorithms ◽  
Fitness Functions ◽  
Noisy Fitness

scholarly journals An Efficient Functional Test Generation Method For Processors Using Genetic Algorithms

2015 ◽  
Vol 66 (4) ◽  
pp. 185-193 ◽  
Author(s):  
Ján Hudec ◽  
Elena Gramatová
Keyword(s):  
Genetic Algorithms ◽  
Test Generation ◽  
Functional Test ◽  
Evolutionary Strategies ◽  
Instruction Set ◽  
Functional Tests ◽  
Automatic Test Generation ◽  
Code Coverage ◽  
Fitness Functions

Abstract The paper presents a new functional test generation method for processors testing based on genetic algorithms and evolutionary strategies. The tests are generated over an instruction set architecture and a processor description. Such functional tests belong to the software-oriented testing. Quality of the tests is evaluated by code coverage of the processor description using simulation. The presented test generation method uses VHDL models of processors and the professional simulator ModelSim. The rules, parameters and fitness functions were defined for various genetic algorithms used in automatic test generation. Functionality and effectiveness were evaluated using the RISC type processor DP32.

scholarly journals Missing data imputation of questionnaires by means of genetic algorithms with different fitness functions

2017 ◽  
Vol 311 ◽  
pp. 704-717 ◽  
Author(s):  
Celestino Ordóñez Galán ◽  
Fernando Sánchez Lasheras ◽  
Francisco Javier de Cos Juez ◽  
Antonio Bernardo Sánchez
Keyword(s):  
Genetic Algorithms ◽  
Missing Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Fitness Functions

scholarly journals An Improved Recommender System Solution to Mitigate the Over-Specialization Problem Using Genetic Algorithms

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 242
Author(s):  
Oumaima Stitini ◽  
Soulaimane Kaloun ◽  
Omar Bencharef
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Recommender System ◽  
Fitness Functions ◽  
System Solution ◽  
Recommendation Algorithms ◽  
Alternative Approaches ◽  
Active Research ◽  
Content Based Filtering ◽  
Filtering Approach

Nowadays, recommendation systems offer a method of facilitating the user’s desire. It is useful for recommending items from a variety of areas such as in the e-commerce, medical, education, tourism, and industry domains. The e-commerce area represents the most active research we found, which assists users in locating the things they want. A recommender system can also provide users with helpful knowledge about things that could be of interest. Sometimes, the user gets bored with recommendations which are similar to their profiles, which leads to the over-specialization problem. Over-specialization is caused by limited content data, under which content-based recommendation algorithms suggest goods directly related to the customer profile rather than new things. In this study, we are particularly interested in recommending surprising, new, and unexpected items that may likely be enjoyed by users and will mitigate this limited content. In order to recommend novel and serendipitous items along with familiar items, we need to introduce additional hacks and note of randomness, which can be achieved using genetic algorithms that brings diversity to recommendations being made. This paper describes a Revolutionary Recommender System using a Genetic Algorithm called RRSGA which improves the fitness functions for recommending optimal results. The proposed approach employs a genetic algorithm to address the over-specialization issue of content-based filtering. The proposed method aims to incorporate genetic algorithms that bring variety to recommendations and efficiently adjust and suggest unpredictable and innovative things to the user. Experiments objectively demonstrate that our technology can recommend additional products that every consumer is likely to appreciate. The results of RRSGA have been compared against recommendation results from the content-based filtering approach. The results indicate the effectiveness of RRSGA and its capacity to make more accurate predictions than alternative approaches.

scholarly journals Order-based fitness functions for genetic algorithms applied to relevance feedback

2002 ◽  
Vol 54 (2) ◽  
pp. 152-160 ◽  
Author(s):  
Cristina López-Pujalte ◽  
Vicente P. Guerrero-Bote ◽  
Félix de Moya-Anegón
Keyword(s):  
Genetic Algorithms ◽  
Relevance Feedback ◽  
Fitness Functions
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