**New Applications of Genetic Algorithm**

120 Bio-Inspired Computational Algorithms and Their Applications

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**7** 

*Spain*

**Tune Up of a Genetic Algorithm** 

José Luis Castillo Sequera

**to Group Documentary Collections** 

*University of Alcala, Department of Computer Science, Madrid* 

Both in industry and science there are some real problems regarding the optimization of difficult solution characterized by computational complexity, because the available exact algorithms are inefficient or simply impossible to implement. The metaheuristics (MHs) are a family of approximate methods of general purpose consisting in iterative procedures that guide heuristics, intelligently combining different concepts to explore and exploit properly the search space [12]. Therefore, there are two important factors when designing MHs : intensification and diversification. The diversification generally refers to the ability to visit many different regions of search space, while intensification refers to the ability to obtain high quality solutions in these regions. A search algorithm must achieve a balance between

On the other hand, Information Retrieval (IR) can be defined as the problem of information selection through a storage mechanism in response to user queries [3]. The Information Retrieval Systems (IRS) are a class of information systems that deal with databases composed of documents, and process user's queries by allowing access to relevant information in an appropriate time interval. Theoreticly, a document is a set of textual data, but technological development has led to the proliferation of multimedia

Genetic Algorithms (GAs) are inspired by MHs in the genetic processes of natural organisms and in the principles of natural evolution of populations [2]. The basic idea is to maintain a population of chromosomes, which represent candidate solutions to a specific problem , that evolve over time through a process of competition and controlled variation. One of the most important components of GAs is the crossover operator [7]. Considering all GA must have a balance between intensification and diversification that is capable of augmenting the search for the optimal, the crossover operator is often regarded as a key piece to improve the intensification of a local optimum. Besides, through the evolutionary process, every so often there are species that have undergone a change (mutation) of chromosome, due to certain evolution factors, as the mutation operator is a key factor in

Efficiently assigning GA parameters optimizes both the quality of the solutions and the resources required by the algorithm [13]. This way, we can obtain a powerful search

these two factors so as to successfully solve the problem addressed.

ensuring that diversification, and finding all the optimum feasible regions.

**1. Introduction** 

documents [4].
