scholarly journals Experiments with Hybrid Genetic Algorithm for the Grey Pattern Problem

Informatica ◽  
2006 ◽  
Vol 17 (2) ◽  
pp. 237-258 ◽  
Author(s):  
Alfonsas Misevičius
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Grey Pattern ◽  
Pattern Problem

scholarly journals Experimental Analysis of Hybrid Genetic Algorithm for the Grey Pattern Quadratic Assignment Problem

2019 ◽  
Vol 48 (2) ◽  
pp. 335-356
Author(s):  
Evelina Stanevičienė ◽  
Alfonsas Misevičius ◽  
Armantas Ostreika
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Quadratic Assignment Problem ◽  
Hybrid Genetic Algorithm ◽  
Quadratic Assignment ◽  
Initial Population ◽  
Local Improvement ◽  
Grey Pattern ◽  
Overall Performance ◽  
Selection Of

In this paper, we present the results of the extensive computational experiments with the hybrid genetic algorithm (HGA) for solving the grey pattern quadratic assignment problem (GP-QAP). The experiments are on the basis of the component-based methodology where the important algorithmic ingredients (features) of HGA are chosen and carefully examined. The following components were investigated: initial population, selection of parents, crossover procedures, number of offspring per generation, local improvement, replacement of population, population restart). The obtained results of the conducted experiments demonstrate how the methodical redesign (reconfiguration) of particular components improves the overall performance of the hybrid genetic algorithm.

scholarly journals TESTING OF CROSSOVER OPERATORS FOR THE GREY PATTERN PROBLEM

2006 ◽  
Vol 12 (1) ◽  
pp. 37-43
Author(s):  
Alfonsas Misevičius
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Search Process ◽  
Great Success ◽  
Main Attention ◽  
Genetic Search ◽  
Combinatorial Optimization Problems ◽  
Grey Pattern ◽  
Pattern Problem

Recently genetic algorithms (GAs) are a great success in solving combinatorial optimization problems. In this paper the performance issues related to the genetic search in the context of the grey pattern problem (GPP) are discussed. The main attention is paid to the investigation of the solution recombination, i.e. crossover operators, which play an important role developing robust genetic algorithms. We implemented seven crossover operators within the hybrid genetic algorithm (HGA) framework, and carried out the extensive experiments in order to test the influence of the recombination operators on the genetic search process. The results obtained from the experimentation with GPP test instances (benchmarks) demonstrate promising efficiency of so‐called multiple parent crossover which is based on a special type of recombination of several solutions‐parents.

Design of GA and Ontology based NLP Frameworks for Online Opinion Mining

2019 ◽  
Vol 13 (2) ◽  
pp. 159-165
Author(s):  
Manik Sharma ◽  
Gurvinder Singh ◽  
Rajinder Singh
Keyword(s):  
Genetic Algorithm ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Opinion Mining ◽  
Hybrid Genetic Algorithm ◽  
Online Reviews ◽  
Middle Tier ◽  
Complete Set ◽  
Mining Model

Background: For almost every domain, a tremendous degree of data is accessible in an online and offline mode. Billions of users are daily posting their views or opinions by using different online applications like WhatsApp, Facebook, Twitter, Blogs, Instagram etc. Objective: These reviews are constructive for the progress of the venture, civilization, state and even nation. However, this momentous amount of information is useful only if it is collectively and effectively mined. Methodology: Opinion mining is used to extract the thoughts, expression, emotions, critics, appraisal from the data posted by different persons. It is one of the prevailing research techniques that coalesce and employ the features from natural language processing. Here, an amalgamated approach has been employed to mine online reviews. Results: To improve the results of genetic algorithm based opining mining patent, here, a hybrid genetic algorithm and ontology based 3-tier natural language processing framework named GAO_NLP_OM has been designed. First tier is used for preprocessing and corrosion of the sentences. Middle tier is composed of genetic algorithm based searching module, ontology for English sentences, base words for the review, complete set of English words with item and their features. Genetic algorithm is used to expedite the polarity mining process. The last tier is liable for semantic, discourse and feature summarization. Furthermore, the use of ontology assists in progressing more accurate opinion mining model. Conclusion: GAO_NLP_OM is supposed to improve the performance of genetic algorithm based opinion mining patent. The amalgamation of genetic algorithm, ontology and natural language processing seems to produce fast and more precise results. The proposed framework is able to mine simple as well as compound sentences. However, affirmative preceded interrogative, hidden feature and mixed language sentences still be a challenge for the proposed framework.

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