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

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.

OntoQuery-An Evolutionary Approach for Query Formation and Information Retrieval

2011 ◽  
pp. 140-160
Author(s):  
Sheng-Uei Guan ◽  
Chang Ching Chng ◽  
Fangming Zhu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Information Retrieval ◽  
Query Optimization ◽  
Formation Process ◽  
Relevance Feedback ◽  
Fitness Function ◽  
Optimization Techniques ◽  
Retrieval Scheme

This chapter proposes the establishment of OntoQuery in an m-commerce agent framework. OntoQuery represents a new query formation approach that combines the usage of ontology and keywords. This approach takes advantage of the tree pathway structure in ontology to form queries visually and efficiently. Also, it uses keywords to complete the query formation process more efficiently. Present query optimization techniques like relevance feedback use expensive iterations. The proposed information retrieval scheme focuses on using genetic algorithms to improve computational effectiveness. Mutations are done on queries formed in the earlier part by replacing terms with synonyms. Query optimization techniques used include query restructuring by logical terms and numerical constraints replacement. Also, the fitness function of the genetic algorithm is defined by three elements, number of documents retrieved, quality of documents, and correlation of queries. The number and quality of documents retrieved give the basic strength of a mutated query.

scholarly journals HIGHER EPISTASIS IN GENETIC ALGORITHMS

2008 ◽  
Vol 77 (2) ◽  
pp. 225-243 ◽  
Author(s):  
M. T. IGLESIAS ◽  
V. S. PEÑARANDA ◽  
C. VIDAL ◽  
A. VERSCHOREN
Keyword(s):  
Genetic Algorithms ◽  
Upper Bound ◽  
Fitness Function ◽  
Search Space ◽  
Natural Generalization ◽  
Fitness Functions

AbstractWe study thek-epistasis of a fitness function over a search space. This concept is a natural generalization of that of epistasis, previously considered by Davidor, Suys and Verschoren and Van Hove and Verschoren [Y. Davidor, in:Foundations of genetic algorithms, Vol. 1, (1991), pp. 23–25; D. Suys and A. Verschoren, ‘Proc Int. Conf. on Intelligent Technologies in Human-Related Sciences (ITHURS’96), Vol. II (1996), pp. 251–258; H. Van Hove and A. Verschoren,Comput. Artificial Intell.14(1994), 271–277], for example. We completely characterize fitness functions whosek-epistasis is minimal: these are exactly the functions of orderk. We also obtain an upper bound for thek-epistasis of nonnegative fitness functions.

Application of genetic algorithms and fuzzy control to a combined sewer pumping station

1999 ◽  
Vol 39 (9) ◽  
pp. 217-224 ◽  
Author(s):  
S. Yagi ◽  
S. Shiba
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Control ◽  
Fuzzy Inference ◽  
Fitness Function ◽  
Pumping Station ◽  
Fitness Functions ◽  
Control Rules ◽  
Combined Sewer ◽  
Pumping Rates ◽  
Input Variables

In the present study, fuzzy logic control and genetic algorithms are applied to achieve improved pump operations in a combined sewer pumping station. Pumping rates are determined by fuzzy inference and fuzzy control rules corresponding to input variables. Genetic algorithms are used to automatically improve the fuzzy control rules through genetic operations such as selection, crossover and mutation. The effects of different fitness functions and learning conditions are investigated using a stormwater runoff model. It is found that current pump operations can be improved by adding the sewer water quality to the input variables and to the fitness function; the improved operations can reduce not only floods in the drainage area but also pollutant loads discharged to the receiving waters.

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