A review on the application of evolutionary computation to information retrieval
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paper_title: Handbook of Evolutionary Computation

paper_content:

From the Publisher: ::: Many scientists and engineers now use the paradigms of evolutionary computation (genetic agorithms, evolution strategies, evolutionary programming, genetic programming, classifier systems, and combinations or hybrids thereof) to tackle problems that are either intractable or unrealistically time consuming to solve through traditional computational strategies. Recently there have been vigorous initiatives to promote cross-fertilization between the EC paradigms, and also to combine these paradigms with other approaches such as neural networks to create hybrid systems with enhanced capabilities. To address the need for speedy dissemination of new ideas in these fields, and also to assist in cross-disciplinary communications and understanding, Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to EC fundamentals, models, algorithms and applications. This work is intended to become the standard reference resource for the evolutionary computation community. The Handbook of Evolutionary Computation will be available in loose-leaf print form, as well as in an electronic version that combines both CD-ROM and on-line (World Wide Web) acess to its contents. Regularly published supplements will be available on a subscription basis.
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paper_title: OUTLINE OF A GENERAL PROBABILISTIC RETRIEVAL MODEL

paper_content:

For reasons of technical convenience, current retrieval algorithms based on probabilistic reasoning are derived from models that assume patrons evaluate documents using a two value relevance scale. This paper extends the theory by describing a model which includes a more general relevance scale. This model permits a re‐examination of the earlier theory as a special case of that developed here and leads to a more satisfying interpretation of the ranking principle of the earlier models.
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paper_title: Fuzzy Approaches to Extend Boolean Information Retrieval

paper_content:

Many commercial information retrieval systems use the Boolean information retrieval model. Some extensions have been designed to provide for the limitations this model presents without proceeding to its complete redesign. Fuzzy set theory has been employed as a suitable tool for this purpose, especially for dealing with imprecision and vagueness in the information retrieval activity. The first extensions of the Boolean query language within this formal framework introduced numeric query weights. Recently, the authors have proposed linguistic extensions which manage user vagueness in formulating queries directly. They have also defined a document representation which more effectively expresses documents’ information content.
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paper_title: Handbook of Evolutionary Computation

paper_content:

From the Publisher: ::: Many scientists and engineers now use the paradigms of evolutionary computation (genetic agorithms, evolution strategies, evolutionary programming, genetic programming, classifier systems, and combinations or hybrids thereof) to tackle problems that are either intractable or unrealistically time consuming to solve through traditional computational strategies. Recently there have been vigorous initiatives to promote cross-fertilization between the EC paradigms, and also to combine these paradigms with other approaches such as neural networks to create hybrid systems with enhanced capabilities. To address the need for speedy dissemination of new ideas in these fields, and also to assist in cross-disciplinary communications and understanding, Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to EC fundamentals, models, algorithms and applications. This work is intended to become the standard reference resource for the evolutionary computation community. The Handbook of Evolutionary Computation will be available in loose-leaf print form, as well as in an electronic version that combines both CD-ROM and on-line (World Wide Web) acess to its contents. Regularly published supplements will be available on a subscription basis.
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paper_title: User-Based Document Clustering by Redescribing Subject Descriptions with a Genetic Algorithm.

paper_content:

Information retrieval systems have used clustering of documents and queries to improve both retrieval efficiency and retrieval effectiveness. Normally, clustering involves grouping together static descriptions of documents by their similarity to each other, though user-based clustering suggests that usage patterns concerning co-relevance can form a basis for clustering. This article reports that clusters of co-relevant documents obtain increasingly similar descriptions when a genetic algorithm is used to adapt subject descriptions so that documents become more effective in matching relevant queries and failing to match nonrelevant queries. As a result of the increased similarity, clustering algorithms can more accurately group documents into useful clusters. The findings of this work were reached through simulation experiments. © 1991 John Wiley & Sons, Inc.
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paper_title: Crossover improvement for the genetic algorithm in information retrieval

paper_content:

Genetic algorithms (GAs) search for good solutions to a problem by operations inspired from the natural selection of living beings. Among their many uses, we can count information retrieval (IR). In this field, the aim of the GA is to help an IR system to find, in a huge documents text collection, a good reply to a query expressed by the user. The analysis of phenomena seen during the implementation of a GA for IR has brought us to a new crossover operation. This article introduces this new operation and compares it with other learning methods.
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paper_title: Personalization of search engine services for effective retrieval and knowledge management

paper_content:

The Internet and corporate intranets provide far more information than anybody can absorb. People use search engines to find the information they require. However, these systems tend to use only one fixed term weighting strategy regardless of the context to which it applies, posing serious performance problems when characteristics of different users, queries, and text collections are taken into consideration. In this paper, we argue that the term weighting strategy should be context specific, that is, different term weighting strategies should be applied to different contexts, and we propose a new systematic approach that can automatically generate term weighting strategies for different contexts based on genetic programming (GP). The new proposed framework was tested on TREC data and the results are very promising.
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paper_title: GENERATION OF EQUIFREQUENT GROUPS OF WORDS USING A GENETIC ALGORITHM

paper_content:

Genetic algorithms are a class of non‐deterministic algorithms that derive from Darwinian evolution and that provide good, though not necessarily optimal, solutions to combinatorial problems. We describe their application to the identification of characteristics that occur approximately equifrequently in a database, using two different methods for the creation of the chromosome data structures that lie at the heart of a genetic algorithm. Experiments with files of English and Turkish text suggest that the genetic algorithm developed here can produce results superior to those produced by existing non‐deterministic algorithms; however, the results are inferior to those produced by an existing deterministic algorithm.
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paper_title: A GA-P algorithm to automatically formulate extended Boolean queries for a fuzzy information retrieval system

paper_content:

Although the fuzzy retrieval model constitutes a powerful extension of the Boolean one, being able to deal with the imprecision and subjectivity existing in the Information Retrieval process, users are not usually able to express their query requirements in the form of an extended Boolean query including weights. To solve this problem, different tools to assist the user in the query formulation have been proposed. In this paper, the genetic algorithm-programming technique is considered to build an algorithm of this kind that will be able to automatically learn weighted queries -modeling the user's needs- for a fuzzy information retrieval system by applying an off-line adaptive process starting from a set of relevant documents.
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paper_title: Analyzing the performance of a multiobjective GA-P algorithm for learning fuzzy queries in a machine learning environment

paper_content:

The fuzzy information retrieval model was proposed some years ago to solve several limitations of the Boolean model without a need of a complete redesign of the information retrieval system. However, the complexity of the fuzzy query language makes it difficult to formulate user queries. Among other proposed approaches to solve this problem, we find the Inductive Query by Example (IQBE) framework, where queries are automatically derived from sets of documents provided by the user. In this work we test the applicability of a multiobjective evolutionary IQBE technique for fuzzy queries in a machine learning environment. To do so, the Cranfield documentary collection is divided into two different document sets, labeled training and test, and the algorithm is run on the former to obtain several queries that are then validated on the latter.
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paper_title: AN UPPERBOUND TO THE PERFORMANCE OF RANKED‐OUTPUT SEARCHING: OPTIMAL WEIGHTING OF QUERY TERMS USING A GENETIC ALGORITHM

paper_content:

This paper describes the development of a genetic algorithm (GA) for the assignment of weights to query terms in a ranked‐output document retrieval system. The GA involves a fitness function that is based on full relevance information, and the rankings resulting from the use of these weights are compared with the Robertson‐Sparck Jones F4 retrospective relevance weight. Extended experiments with seven document test collections show that the ga can often find weights that are slightly superior to those produced by the deterministic weighting scheme. That said, there are many cases where the two approaches give the same results, and a few cases where the F4 weights are superior to the ga weights. Since the ga has been designed to identify weights yielding the best possible level of retrospective performance, these results indicate that the F4 weights provide an excellent and practicable alternative. Evidence is presented to suggest that negative weights may play an important role in retrospective relevance weighting.
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paper_title: Evolutionary Learning of Boolean Queries by Multiobjective Genetic Programming

paper_content:

The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimization of any of its components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of these applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme. In this paper, we will tackle with a usual information retrieval problem, the automatic derivation of Boolean queries, by incorporating a well known Pareto-based multiobjective evolutionary approach, MOGA, into a previous proposal of a genetic programming technique for this task.
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paper_title: A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

paper_content:

Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.
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paper_title: Multiple query evaluation based on an enhanced genetic algorithm

paper_content:

Recent studies suggest that significant improvement in information retrieval performance can be achieved by combining multiple representations of an information need. The paper presents a genetic approach that combines the results from multiple query evaluations. The genetic algorithm aims to optimise the overall relevance estimate by exploring different directions of the document space. We investigate ways to improve the effectiveness of the genetic exploration by combining appropriate techniques and heuristics known in genetic theory or in the IR field. Indeed, the approach uses a niching technique to solve the relevance multimodality problem, a relevance feedback technique to perform genetic transformations on query formulations and evolution heuristics in order to improve the convergence conditions of the genetic process. The effectiveness of the global approach is demonstrated by comparing the retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation performed on a subset of TREC-4 using the Mercure IRS. Moreover, experimental results show the positive effect of the various techniques integrated to our genetic algorithm model.
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paper_title: On using genetic algorithms for multimodal relevance optimization in information retrieval

paper_content:

This article presents a genetic relevance optimization process performed in an information retrieval system. The process uses genetic techniques for solving multimodal problems (niching) and query reformulation techniques commonly used in information retrieval. The niching technique allows the process to reach different relevance regions of the document space. Query reformulation techniques represent domain knowledge integrated in the genetic operators structure to improve the convergence conditions of the algorithm. Experimental analysis performed using a TREC subcollection validates our approach.
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paper_title: Query modification using genetic algorithms in vector space models

paper_content:

The ability of information retrieval system parameters on the basis of user's relevance feedback for improving the retrieval effectiveness has become an important and also an active research area. One such adjustment is to modify the query which reflects the user's information request via the judgment of the previously retrieved results. In this paper we present our idea, based on an adaptive technique of "genetic algorithms," for modifying a user's query to improve the retrieval results. The effectiveness of query modification has been tested on the Cranfield Collection, a classical set of documents, queries and known relevance responses to each query. Results of this study show that the method is highly effective, achieving substantially higher precision levels at each fixed recall level than those have been reported by other research groups.
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paper_title: Automatic Learning of Multiple Extended Boolean Queries by Multiobjective GA-P Algorithms

paper_content:

In this contribution, a new Inductive Query by Example process is proposed to automatically derive extended Boolean queries for fuzzy information retrieval systems from a set of relevant documents provided by a user. The novelty of our approach is that it is able to simultanously generate several queries with a different precision-recall tradeoff in a single run. To do so, it is based on an advanced. evolutionary algorithm, GA-P, specially designed to tackle with multiobjective problems by means of a Pareto-based multiobjective technique. The performance of the new proposal will be tested on the usual Cranfield collection and compared to the well-known Kraft et al.’s process.
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paper_title: Genetic Approach to Query Space Exploration

paper_content:

This paper describes a genetic algorithm approach for intelligent information retrieval. The goal is to find an optimal set of documents which best matches the user's needs by exploring and exploiting the document space. More precisely, we define a specific genetic algorithm for information retrieval based on knowledge based operators and guided by a heuristic for relevance multi-modality problem solving. Experiments with TREC-6 French data and queries show the effectiveness of our approach.
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paper_title: Applying genetic algorithms to query optimization in document retrieval

paper_content:

Abstract This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. One of the contributions of the paper is to combine the Bigram (Chen, A., He, J., Xu, L., Gey, F. C., & Meggs, J. 1997. Chinese text retrieval without using a dictionary , ACM SIGIR’97, Philadelphia, PA, USA, pp. 42–49; Yang, Y.-Y., Chang, J.-S., & Chen, K.-J. 1993), Document automatic classification and ranking , Master thesis, Department of Computer Science, National Tsing Hua University) model and PAT-tree structure (Chien, L.-F., Huang, T.-I., & Chien, M.-C. 1997 Pat-tree-based keyword extraction for Chinese information retrieval , ACM SIGIR’97, Philadelphia, PA, US, pp. 50–59) to retrieve keywords. The approach extracts bigrams from documents and uses the bigrams to construct a PAT-tree to retrieve keywords. The proposed approach can retrieve any type of keywords such as technical keywords and a person’s name. Effectiveness of the proposed approach is demonstrated by comparing how effective are the keywords found by both this approach and the PAT-tree based approach. This comparison reveals that our keyword retrieval approach is as accurate as the PAT-tree based approach, yet our approach is faster and uses less memory. The study then applies genetic algorithms to tune the weight of retrieved keywords. Moreover, several documents obtained from web sites are tested and experimental results are compared with those of other approaches, indicating that the proposed approach is highly promising for applications.
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paper_title: Order-based fitness functions for genetic algorithms applied to relevance feedback

paper_content:

Recently there have been appearing new applications of genetic algorithms to information retrieval, most of them specifically to relevance feedback. The evolution of the possible solutions are guided by fitness functions that are designed as measures of the goodness of the solutions. These functions are naturally the key to achieving a reasonable improvement, and which function is chosen most distinguishes one experiment from another. In previous work, we found that, among the functions implemented in the literature, the ones that yield the best results are those that take into account not only when documents are retrieved, but also the order in which they are retrieved. Here, we therefore evaluate the efficacy of a genetic algorithm with various order-based fitness functions for relevance feedback (some of them of our own design), and compare the results with the Ide dec-hi method, one of the best traditional methods.
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paper_title: Query modification using genetic algorithms in vector space models

paper_content:

The ability of information retrieval system parameters on the basis of user's relevance feedback for improving the retrieval effectiveness has become an important and also an active research area. One such adjustment is to modify the query which reflects the user's information request via the judgment of the previously retrieved results. In this paper we present our idea, based on an adaptive technique of "genetic algorithms," for modifying a user's query to improve the retrieval results. The effectiveness of query modification has been tested on the Cranfield Collection, a classical set of documents, queries and known relevance responses to each query. Results of this study show that the method is highly effective, achieving substantially higher precision levels at each fixed recall level than those have been reported by other research groups.
---
paper_title: Query modification using genetic algorithms in vector space models

paper_content:

The ability of information retrieval system parameters on the basis of user's relevance feedback for improving the retrieval effectiveness has become an important and also an active research area. One such adjustment is to modify the query which reflects the user's information request via the judgment of the previously retrieved results. In this paper we present our idea, based on an adaptive technique of "genetic algorithms," for modifying a user's query to improve the retrieval results. The effectiveness of query modification has been tested on the Cranfield Collection, a classical set of documents, queries and known relevance responses to each query. Results of this study show that the method is highly effective, achieving substantially higher precision levels at each fixed recall level than those have been reported by other research groups.
---
paper_title: Applying genetic algorithms to query optimization in document retrieval

paper_content:

Abstract This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. One of the contributions of the paper is to combine the Bigram (Chen, A., He, J., Xu, L., Gey, F. C., & Meggs, J. 1997. Chinese text retrieval without using a dictionary , ACM SIGIR’97, Philadelphia, PA, USA, pp. 42–49; Yang, Y.-Y., Chang, J.-S., & Chen, K.-J. 1993), Document automatic classification and ranking , Master thesis, Department of Computer Science, National Tsing Hua University) model and PAT-tree structure (Chien, L.-F., Huang, T.-I., & Chien, M.-C. 1997 Pat-tree-based keyword extraction for Chinese information retrieval , ACM SIGIR’97, Philadelphia, PA, US, pp. 50–59) to retrieve keywords. The approach extracts bigrams from documents and uses the bigrams to construct a PAT-tree to retrieve keywords. The proposed approach can retrieve any type of keywords such as technical keywords and a person’s name. Effectiveness of the proposed approach is demonstrated by comparing how effective are the keywords found by both this approach and the PAT-tree based approach. This comparison reveals that our keyword retrieval approach is as accurate as the PAT-tree based approach, yet our approach is faster and uses less memory. The study then applies genetic algorithms to tune the weight of retrieved keywords. Moreover, several documents obtained from web sites are tested and experimental results are compared with those of other approaches, indicating that the proposed approach is highly promising for applications.
---
paper_title: Applying genetic algorithms to query optimization in document retrieval

paper_content:

Abstract This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. One of the contributions of the paper is to combine the Bigram (Chen, A., He, J., Xu, L., Gey, F. C., & Meggs, J. 1997. Chinese text retrieval without using a dictionary , ACM SIGIR’97, Philadelphia, PA, USA, pp. 42–49; Yang, Y.-Y., Chang, J.-S., & Chen, K.-J. 1993), Document automatic classification and ranking , Master thesis, Department of Computer Science, National Tsing Hua University) model and PAT-tree structure (Chien, L.-F., Huang, T.-I., & Chien, M.-C. 1997 Pat-tree-based keyword extraction for Chinese information retrieval , ACM SIGIR’97, Philadelphia, PA, US, pp. 50–59) to retrieve keywords. The approach extracts bigrams from documents and uses the bigrams to construct a PAT-tree to retrieve keywords. The proposed approach can retrieve any type of keywords such as technical keywords and a person’s name. Effectiveness of the proposed approach is demonstrated by comparing how effective are the keywords found by both this approach and the PAT-tree based approach. This comparison reveals that our keyword retrieval approach is as accurate as the PAT-tree based approach, yet our approach is faster and uses less memory. The study then applies genetic algorithms to tune the weight of retrieved keywords. Moreover, several documents obtained from web sites are tested and experimental results are compared with those of other approaches, indicating that the proposed approach is highly promising for applications.
---
paper_title: Order-based fitness functions for genetic algorithms applied to relevance feedback

paper_content:

Recently there have been appearing new applications of genetic algorithms to information retrieval, most of them specifically to relevance feedback. The evolution of the possible solutions are guided by fitness functions that are designed as measures of the goodness of the solutions. These functions are naturally the key to achieving a reasonable improvement, and which function is chosen most distinguishes one experiment from another. In previous work, we found that, among the functions implemented in the literature, the ones that yield the best results are those that take into account not only when documents are retrieved, but also the order in which they are retrieved. Here, we therefore evaluate the efficacy of a genetic algorithm with various order-based fitness functions for relevance feedback (some of them of our own design), and compare the results with the Ide dec-hi method, one of the best traditional methods.
---
paper_title: Evolutionary Learning of Boolean Queries by Multiobjective Genetic Programming

paper_content:

The performance of an information retrieval system is usually measured in terms of two different criteria, precision and recall. This way, the optimization of any of its components is a clear example of a multiobjective problem. However, although evolutionary algorithms have been widely applied in the information retrieval area, in all of these applications both criteria have been combined in a single scalar fitness function by means of a weighting scheme. In this paper, we will tackle with a usual information retrieval problem, the automatic derivation of Boolean queries, by incorporating a well known Pareto-based multiobjective evolutionary approach, MOGA, into a previous proposal of a genetic programming technique for this task.
---
paper_title: A GA-P algorithm to automatically formulate extended Boolean queries for a fuzzy information retrieval system

paper_content:

Although the fuzzy retrieval model constitutes a powerful extension of the Boolean one, being able to deal with the imprecision and subjectivity existing in the Information Retrieval process, users are not usually able to express their query requirements in the form of an extended Boolean query including weights. To solve this problem, different tools to assist the user in the query formulation have been proposed. In this paper, the genetic algorithm-programming technique is considered to build an algorithm of this kind that will be able to automatically learn weighted queries -modeling the user's needs- for a fuzzy information retrieval system by applying an off-line adaptive process starting from a set of relevant documents.
---
paper_title: Analyzing the performance of a multiobjective GA-P algorithm for learning fuzzy queries in a machine learning environment

paper_content:

The fuzzy information retrieval model was proposed some years ago to solve several limitations of the Boolean model without a need of a complete redesign of the information retrieval system. However, the complexity of the fuzzy query language makes it difficult to formulate user queries. Among other proposed approaches to solve this problem, we find the Inductive Query by Example (IQBE) framework, where queries are automatically derived from sets of documents provided by the user. In this work we test the applicability of a multiobjective evolutionary IQBE technique for fuzzy queries in a machine learning environment. To do so, the Cranfield documentary collection is divided into two different document sets, labeled training and test, and the algorithm is run on the former to obtain several queries that are then validated on the latter.
---
paper_title: A new evolutionary algorithm combining simulated annealing and genetic programming for relevance feedback in fuzzy information retrieval systems

paper_content:

Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.
---
paper_title: Multiple query evaluation based on an enhanced genetic algorithm

paper_content:

Recent studies suggest that significant improvement in information retrieval performance can be achieved by combining multiple representations of an information need. The paper presents a genetic approach that combines the results from multiple query evaluations. The genetic algorithm aims to optimise the overall relevance estimate by exploring different directions of the document space. We investigate ways to improve the effectiveness of the genetic exploration by combining appropriate techniques and heuristics known in genetic theory or in the IR field. Indeed, the approach uses a niching technique to solve the relevance multimodality problem, a relevance feedback technique to perform genetic transformations on query formulations and evolution heuristics in order to improve the convergence conditions of the genetic process. The effectiveness of the global approach is demonstrated by comparing the retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation performed on a subset of TREC-4 using the Mercure IRS. Moreover, experimental results show the positive effect of the various techniques integrated to our genetic algorithm model.
---
paper_title: On using genetic algorithms for multimodal relevance optimization in information retrieval

paper_content:

This article presents a genetic relevance optimization process performed in an information retrieval system. The process uses genetic techniques for solving multimodal problems (niching) and query reformulation techniques commonly used in information retrieval. The niching technique allows the process to reach different relevance regions of the document space. Query reformulation techniques represent domain knowledge integrated in the genetic operators structure to improve the convergence conditions of the algorithm. Experimental analysis performed using a TREC subcollection validates our approach.
---
paper_title: Automatic Learning of Multiple Extended Boolean Queries by Multiobjective GA-P Algorithms

paper_content:

In this contribution, a new Inductive Query by Example process is proposed to automatically derive extended Boolean queries for fuzzy information retrieval systems from a set of relevant documents provided by a user. The novelty of our approach is that it is able to simultanously generate several queries with a different precision-recall tradeoff in a single run. To do so, it is based on an advanced. evolutionary algorithm, GA-P, specially designed to tackle with multiobjective problems by means of a Pareto-based multiobjective technique. The performance of the new proposal will be tested on the usual Cranfield collection and compared to the well-known Kraft et al.’s process.
---
paper_title: Genetic Approach to Query Space Exploration

paper_content:

This paper describes a genetic algorithm approach for intelligent information retrieval. The goal is to find an optimal set of documents which best matches the user's needs by exploring and exploiting the document space. More precisely, we define a specific genetic algorithm for information retrieval based on knowledge based operators and guided by a heuristic for relevance multi-modality problem solving. Experiments with TREC-6 French data and queries show the effectiveness of our approach.
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paper_title: Effective information retrieval using genetic algorithms based matching functions adaptation

paper_content:

Knowledge intensive organizations have vast array of information contained in large document repositories. With the advent of E-commerce and corporate intranets/extranets, these repositories are expected to grow at a fast pace. This explosive growth has led to huge, fragmented, and unstructured document collections. Although it has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. This paper addresses the issue of improving retrieval performance (in terms of precision and recall) for retrieval from document collections. There are three important paradigms of research in the area of information retrieval (1R): Probabilistic IR, Knowledge-based IR, and, Artificial Intelligence based techniques like neural networks and symbolic learning. Very few researcher have tried to use evolutionary algorithms like genetic algorithms (GAs). Previous attempts at using GAs have concentrated on modifying document representations or modifying query representations. This work looks at the possibility of applying GAs to adapt various matching functions. It is hoped that such an adaptation of the matching functions in lead to a better retrieval performance than that obtained by using a single matching function. An overall matching function is treated as an weighted combination of scores produced by individual matching functions. This overall score is asked to rank and retrieve documents. Weights associated with individual functions are searched using Genetic Algorithms. The idea is tested on a real document collection called the Cranfield collection. The results look very encouraging.
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paper_title: Automatic generation of a matching function by genetic programming for effective information retrieval

paper_content:

With the advent of the Internet, online resources are increasingly available. Many users choose popular search engines to perform an online search to satisfy their information need. However, these search engines tend to turn up many non-relevant documents, which make their retrieval precision very low. How to find appropriate ranking metrics to retrieve more relevant documents and fewer non-relevant documents for users remains a big challenge to the information retrieval community. In this paper, we propose a new framework that combines the merits of genetic programming and relevance feedback techniques to automatically generate and refine the matching functions used for document ranking. This approach overcomes the shortcoming of traditional ranking algorithms using a fixed ranking strategy. It also gives some new ideas and hints for information retrieval professionals.
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paper_title: Discovery of context-specific ranking functions for effective information retrieval using genetic programming

paper_content:

The Internet and corporate intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. We argue that the ranking strategy should be context specific and we propose a , new systematic method that can automatically generate ranking strategies for different contexts based on genetic programming (GP). The new method was tested on TREC data and the results are very promising.
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paper_title: Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns

paper_content:

Local similarity pattern (LSP) is proposed as a new method for computing image similarity. Similarity of a pair of images is expressed in terms of similarities of the corresponding image regions, obtained by the uniform partitioning of the image area. Different from the existing methods, each region-wise similarity is computed using a different combination of image features (color, shape, and texture). In addition, a method for optimizing the LSP-based similarity computation, based on genetic algorithm, is proposed, and incorporated in the relevance feedback mechanism, allowing the user to automatically specify LSP-based queries. LSP is evaluated on five test databases totalling around 2500 images of various sorts. Compared with both the conventional and the relevance feedback methods for computing image similarity, LSP brings in average over 11% increase in the retrieval precision. Results suggest that the proposed LSP method, allowing comparison of different image regions using different similarity criteria, is more suited for modeling the human perception of image similarity than the existing methods.
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paper_title: A human-oriented image retrieval system using interactive genetic algorithm

paper_content:

Content-based image retrieval has been actively studied in several fields. This provides more effective management and retrieval of images than the keyword-based approach. However, most of the conventional methods lack the capability to effectively incorporate human intuition and emotion into retrieving images. It is difficult to obtain satisfactory results when the user wants the image that cannot be explicitly described or can be requested only based on impression. In order to solve this problem and supplement the lack of the user's expression capability, we have developed an image retrieval system based on human preference and emotion by using an interactive genetic algorithm (IGA). This system extracts the feature from images by wavelet transform, and provides a user-friendly means to retrieve an image from a large database when the user cannot clearly define what the image must be. Therefore, this facilitates the search for the image not only with explicit queries, but also with implicit queries such as "cheerful impression," "gloomy impression," and so on. A thorough experiment with a 2000 image database shows the usefulness of the proposed system.
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paper_title: An image retrieval method based on a genetic algorithm

paper_content:

This paper describes an image retrieval system based on a genetic algorithm with an interactive mechanism that dynamically reflects the subjectivity of individual users in the retrieval results. A technique for enhancing circumstantial dependence that corresponds to a change of the user's mind is also newly presented. In this approach, without explicit representation of the desired image, the image is nevertheless induced through feature selection based on the diverse, fragmentary, and ambiguous subjective intent of the user. This points the way to a powerful image retrieval system that is capable of guessing the subjective intent in the mind of the user and is not dependent on overt representation of images.
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paper_title: Using genetic feature selection for optimizing user profiles

paper_content:

Most of the techniques used in text classification are determined ::: by the occurrences of the words (terms) appearing in the ::: documents, combined with the user feedback over the documents ::: retrieved. However, in our model, the most relevant terms will be ::: selected from a previous fuzzy classification given by the genetic ::: algorithm guided by the user feedback, but using techniques from ::: Machine Learning. A feature selection process is carried out ::: through a Genetic Algorithm in order to find the most ::: discriminatory terms to be stored as the user profile.
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paper_title: Automatically Improving the Accuracy of User Profiles with Genetic Algorithm

paper_content:

With information retrieval systems, bridging the gap between the physical characteristics of data with the user perceptions is challenging. In order to address this challenge, employing user profiles to improve the retrieval accuracy becomes essential. However, the system performance may degrade due to inaccuracy of user profiles. Therefore, for an approach to be effective, it should offer a learning mechanism to correct user input errors. Focusing on an image retrieval application, we utilize the users’ relevance feedback to improve the profiles automatically using genetic algorithms (GA). Our experimental results indicated that the retrieval accuracy is significantly increased using the GA-based learning mechanism.
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paper_title: An Evolutionary Approach to Automatic Web Page Categorization and Updating

paper_content:

Catalogues play an important role in most of the current Web search engines. The catalogues, which organize documents into hierarchical collections, are maintained manually increasing difficult y and costs due to the incessant growing of the WWW. This problem has stimulated many researches to work on automatic categorization of Web documents. In reality, most of these approaches work well either on special types of documents or on restricted set of documents. This paper presents an evolutionary approach useful to construct automatically the catalogue as well as to perform the classification of a Web document. This functionality relies on a genetic-based fuzzy clustering methodology that applies the clustering on the context of the document, as opposite to content-based clustering that works on the complete document information.
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paper_title: Enhancing information retrieval by automatic acquisition of textual relations using genetic programming

paper_content:

We have explored a novel method to find textual relations in electronic documents using genetic programming and semantic networks. This can be used for enhancing information retrieval and simplifying user interfaces. The automatic extraction of relations from text enables easier updating of electronic dictionaries and may reduce interface area both for search input and hit output on small screens such as cell phones and PDAs (Personal Digital Assistants).
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paper_title: Mobile agent evolution computing

paper_content:

Abstract The ecosystem is an evolutionary result of natural laws. Food web (or food chain) embeds a set of computation rules of natural balance. Based on the concepts of food web, one of the laws that we may learn from the natural besides neural networks and genetic algorithms, we propose a theoretical computation model for mobile-agent evolution on the Internet. We define an agent niche overlap graph and agent evolution states. We also propose a set of algorithms, which is used in our multimedia search programs, to simulate agent evolution. Agents are cloned to live on a remote host station based on three different strategies: the brute force strategy, the semi-brute force strategy, and the selective strategy. Evaluations of different strategies are discussed. Guidelines of writing mobile-agent programs are proposed. The technique can be used in distributed information retrieval which allows the computation load to be added to servers, but significantly reduces the traffic of network communication. In the literature of software agents, it is hard to find other similar models. The results of this research only address a small portion of the ice field. We hope that this problem would be further studied in the societies of network communications, multimedia information retrieval, and intelligent systems on the Internet.
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paper_title: An Intelligent Personal Spider (Agent) for Dynamic Internet/Intranet Searching

paper_content:

Abstract As Internet services based on the World-Wide Web become more popular, information overload has become a pressing research problem. Difficulties with search on Internet will worsen as the amount of on-line information increases. A scalable approach to Internet search is critical to the success of Internet services and other current and future National Information Infrastructure (NII) applications. As part of the ongoing Illinois Digital Library Initiative project, this research proposes an intelligent personal spider (agent) approach to Internet searching. The approach, which is grounded on automatic textual analysis and general-purpose search algorithms, is expected to be an improvement over the current static and inefficient Internet searches. In this experiment, we implemented Internet personal spiders based on best first search and genetic algorithm techniques. These personal spiders can dynamically take a user's selected starting homepages and search for the most closely related homepages in the web, based on the links and keyword indexing. A plain, static CGI/HTML-based interface was developed earlier, followed by a recent enhancement of a graphical, dynamic Java-based interface. Preliminary evaluation results and two working prototypes (available for Web access) are presented. Although the examples and evaluations presented are mainly based on Internet applications, the applicability of the proposed techniques to the potentially more rewarding Intranet applications should be obvious. In particular, we believe the proposed agent design can be used to locate organization-wide information, to gather new, time-critical organizational information, and to support team-building and communication in Intranets.
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paper_title: Search engine case study: searching the web using genetic programming and MPI

paper_content:

Abstract The generation of a Web page follows distinct sources for the incorporation of information. The earliest format of these sources was an organized display of known information determined by the page designers' interest and/or design parameters. The sources may have been published in books or other printed literature, or disseminated as general information about the page designer. Due to a growth in Web pages, several new search engines have been developed in addition to the refinement of the already existing ones. The use of the refined search engines, however, still produces an array of diverse information when the same set of keywords are used in a Web search. Some degree of consistency in the search results can be achieved over a period of time when the same search engine is used, yet, most initial Web searches on a given topic are treated as final after some form of refinement/adjustment of the keywords used in the search process. To determine the applicability of a genetic programming (GP) model for the diverse set of Web documents, search strategies behind the current search engines for the World Wide Web were studied. The development of a GP model resulted in a parallel implementation of a pseudo-search engine indexer simulator. The training sets used in this study provided a small snapshot of the computational effort required to index Web documents accurately and efficiently. Future results will be used to develop and implement Web crawler mechanisms that are capable of assessing the scope of this research effort. The GP model results were generated on a network of SUN workstations and an IBM SP2.
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Title: A Review on the Application of Evolutionary Computation to Information Retrieval

Section 1: Introduction
Description 1: Provide an overview of the information age and the challenges in information retrieval (IR). Highlight the increasing efforts to use evolutionary computation (EC) in IR to address these challenges. Outline the structure of the paper.

Section 2: Information Retrieval
Description 2: Define information retrieval and describe the components and models of an information retrieval system. Discuss evaluative criteria for IRSs.

Section 3: Evolutionary Algorithms
Description 3: Describe evolutionary computation and the different types of evolutionary algorithms (EAs), including genetic algorithms, evolution strategies, genetic programming, and evolutionary programming.

Section 4: Applications of Evolutionary Algorithms to Information Retrieval
Description 4: Summarize the various applications of EAs in solving different IR problems, such as automatic document indexing, document and term clustering, and query definition.

Section 5: Automatic Document Indexing
Description 5: Review approaches where EAs are used to learn document descriptions to improve retrieval relevance.

Section 6: Clustering of Documents and Terms
Description 6: Discuss approaches for grouping terms and documents using EAs to enhance IR.

Section 7: Query Definition
Description 7: Explain the use of EAs in query definition as relevance feedback techniques or as Inductive Query by Example (IQBE) algorithms.

Section 8: Matching Function Learning
Description 8: Describe the use of EAs to generate effective similarity measures to improve the retrieval process.

Section 9: Image Retrieval
Description 9: Summarize the combination of EAs with image retrieval to enhance relevance and user preference considerations.

Section 10: Design of User Profiles for Information Retrieval on the Internet
Description 10: Discuss the use of EAs in creating user profiles to personalize and improve search results on the Internet.

Section 11: Web Page Classification
Description 11: Review methods where EAs are applied to classify and organize web documents for better indexing and retrieval.

Section 12: Design of Agents for Internet Searching
Description 12: Summarize approaches that use EAs to enhance the performance and efficiency of search agents on the Internet.

Section 13: Concluding Remarks
Description 13: Provide an overall summary of the applications of EC to IR discussed in the paper and suggest future directions for research.