Large Population or Many Generations for Genetic Algorithms? Implications in Information Retrieval

2000 ◽  
pp. 199-222 ◽  
Author(s):  
Dana Vrajitoru
Keyword(s):  
Genetic Algorithms ◽  
Information Retrieval ◽  
Large Population

Query Formation and Information Retrieval with Ontology

2007 ◽  
pp. 310-323
Author(s):  
Sheng-Uei Guan
Keyword(s):  
Genetic Algorithms ◽  
Information Retrieval ◽  
Query Optimization ◽  
Formation Process ◽  
Retrieval System ◽  
Mobile Commerce ◽  
Information Retrieval System ◽  
Optimization Techniques ◽  
Tree Structure ◽  
Retrieval Scheme

This chapter presents an ontology-based query formation and information retrieval system under the mobile commerce (m-commerce) agent framework. A query formation approach that combines the usage of ontology and keywords is implemented. This approach takes advantage of the tree structure in ontology to form queries visually and efficiently. It also uses additional aids such as keywords to complete the query formation process more efficiently. The proposed information retrieval scheme focuses on using genetic algorithms (GAs) to improve computational effectiveness. Other query optimization techniques used include query restructuring by logical terms and numerical constraints replacement.

scholarly journals Genetic algorithms - variable size populations of chromosomes. An adaptive approach

2018 ◽  
Vol XIX (1) ◽  
pp. 393-399
Author(s):  
Maniu R
Keyword(s):  
Genetic Algorithms ◽  
Negative Impact ◽  
Large Population ◽  
Search Space ◽  
Global Optimum ◽  
Small Population ◽  
Local Optimum ◽  
Variable Size ◽  
Algorithm Operation

The size of the chromosome population is an essential parameter of genetic algorithms. A large population involves a large amount of calculations but provides a complete scroll of the search space and the increased probability of generating a global optimum. A small population size, through the small number of operations required, causes a quick run of the algorithm, with increasing the probability of detecting a local optimum to the detriment of the global one. This paper proposes the use of an adaptive, variable size of chromosome population. We will demonstrate that this approach leads to an acceleration of the algorithm operation, without having a negative impact on the quality of provided solutions.

A Hybrid System Composed of Neural Networks and Genetic Algorithms

2012 ◽  
Vol 3 (4) ◽  
pp. 45-50
Author(s):  
Dumitrescu Mihaela
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Hybrid Systems ◽  
Large Population ◽  
Complex Data ◽  
Optimal Decisions ◽  
Business Efficiency ◽  
Overall Performance ◽  
And Performance ◽  
Better Than

Neural networks are known for their ability to recognize patterns of noisy, complex data and to estimate a nonlinear relationship between them. But the design of neural networks is very complex because it works on the principle of “black box.” The application of genetic algorithms in neural networks with hybrid systems can improve a network’s ability to make predictions. Hybrid systems involve the use of combined techniques, issues and different models in order to achieve the overall performance better than those offered by each solution considered separately. Projections made by hybrid systems have smaller errors and constructed systems are able to automatically select the variables necessary to function effectively. This is possible due to the principle of selective biological function of genetic algorithms. They select from a large population of neural networks the best generations, made their exchange of elements and even mutations to get the most advanced networks. For an evolving and performance changes economic environment are necessary tools that can help make faster optimal decisions and increase the business efficiency.

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.

Modeling Simple Genetic Algorithms

1995 ◽  
Vol 3 (4) ◽  
pp. 453-472 ◽  
Author(s):  
Michael D. Vose
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Asymptotic Behavior ◽  
Finite Population ◽  
Large Population ◽  
Population Models ◽  
Genetic Search ◽  
Simple Genetic Algorithm ◽  
Simple Genetic Algorithms

The infinite- and finite-population models of the simple genetic algorithm are extended and unified, The result incorporates both transient and asymptotic GA behavior. This leads to an interpretation of genetic search that partially explains population trajectories. In particular, the asymptotic behavior of the large-population simple genetic algorithm is analyzed.

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