Variable String Length Genetic Algorithm Research Articles

A multiobjective spatial fuzzy clustering algorithm for image segmentation

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set of non-dominated solutions, the final clustering solution is chosen by a cluster validity index utilizing the non-local spatial information. Moreover, to automatically evolve the number of clusters in the proposed method, a real-coded variable string length technique is used to encode the cluster centers in the chromosomes. The proposed method is applied to synthetic and real images contaminated by noise and compared with k-means, fuzzy c-means, two fuzzy c-means clustering algorithms with spatial information and a multiobjective variable string length genetic fuzzy clustering algorithm. The experimental results show that the proposed method behaves well in evolving the number of clusters and obtaining satisfactory performance on noisy image segmentation.

An Improved Genetic Algorithm for Text Clustering

The genetic algorithm (GA) is a self-adapted probability search method used to solve optimization problems, which has been applied widely in science and engineering. In this paper, we propose an improved variable string length genetic algorithm (IVGA) for text clustering. Our algorithm has been exploited for automatically evolving the optimal number of clusters as well as providing proper data set clustering. The chromosome is encoded by special indices to indicate the location of each gene. More effective version of evolutional steps can automatically adjust the influence between the diversity of the population and selective pressure during generations. The superiority of the improved genetic algorithm over conventional variable string length genetic algorithm (VGA) is demonstrated by providing proper text clustering.

Automatic fuzzy partitioning approach using Variable string length Artificial Bee Colony (VABC) algorithm

Swarm intelligence based automatic fuzzy clustering is recently an important and interesting unsupervised learning problem. In this article, an automatic fuzzy clustering technique is proposed based on a novel version of Artificial Bee Colony (ABC) algorithm.The idea of variable length genotypes is introduced to the ABC, and a novel version of ABC, called Variable string length Artificial Bee Colony (VABC) algorithm, is proposed. The VABC algorithm is derived from the ABC by redefining or modifying some operations in the ABC: the fixed length strings are represented by using variable length strings, the scheme for producing candidate solutions is modified, and some mutation operations are introduced. Use of VABC allows the encoding of variable number of clusters. This makes the VABC based Fuzzy C-Means clustering technique (VABC-FCM) not require a priori specification of the number of clusters. Moreover, the VABC-FCM has powerful global search ability under rational parameter setting. Some artificial data sets and real-life data sets are applied to validate the performance of VABC-FCM. The experimental results show that VABC-FCM can automatically evolve the optimal number of clusters and find proper fuzzy partitioning for these data sets when a rational validity index is adopted. Finally, the performance of VABC-FCM is compared with those of the Variable string length Genetic Algorithm based Fuzzy C-Means clustering (VGA-FCM), Particle Swarm Optimization algorithm based Fuzzy C-Means clustering (PSO-FCM), and Differential Evolutional algorithm based Fuzzy C-Means clustering (DE-FCM). The results show that the VABC-FCM outperforms VGA-FCM, PSO-FCM and DE-FCM in most of the cases.

IVGA3D: De Novo Ligand Design Using a Variable Sized Tree Representation

Rational drug design is an important step towards effective patient care enabling lead molecule discovery in a relatively faster and inexpensive way. This article describes a variable string length genetic algorithm with domain specific operators for de novo ligand design. The algorithm first mines the active site of the given protein receptor whose geometry and chemical composition guides the ligand building. Active site mined by the algorithm is compared with two more established active sites detecting schemes to evaluate its efficiency. Various combinations from a suite of forty one fragments are mined to design the ligands. Bond stretching, angle bending, torsional terms, van der Waals and electrostatic interaction energy with distance dependent dielectric constant contribute are used to compute the internal energy of the ligand and the interaction energy of the ligand receptor complex. Forty one fragments are used to design the ligands. Experimental results are provided for HIV-1 Protease, HIV-1 Nef and Thrombin demonstrating the superiority of the proposed scheme vis-a-vis three other approaches. Comparison with known inhibitors also demonstrates the effectiveness of the proposed approach.

Towards improving fuzzy clustering using support vector machine: Application to gene expression data

Recent advancement in microarray technology permits monitoring of the expression levels of a large set of genes across a number of time points simultaneously. For extracting knowledge from such huge volume of microarray gene expression data, computational analysis is required. Clustering is one of the important data mining tools for analyzing such microarray data to group similar genes into clusters. Researchers have proposed a number of clustering algorithms in this purpose. In this article, an attempt has been made in order to improve the performance of fuzzy clustering by combining it with support vector machine (SVM) classifier. A recently proposed real-coded variable string length genetic algorithm based clustering technique and an iterated version of fuzzy C-means clustering have been utilized in this purpose. The performance of the proposed clustering scheme has been compared with that of some well-known existing clustering algorithms and their SVM boosted versions for one simulated and six real life gene expression data sets. Statistical significance test based on analysis of variance (ANOVA) followed by posteriori Tukey–Kramer multiple comparison test has been conducted to establish the statistical significance of the superior performance of the proposed clustering scheme. Moreover biological significance of the clustering solutions have been established.

Genetic algorithm for text clustering based on latent semantic indexing

In this paper, we develop a genetic algorithm method based on a latent semantic model (GAL) for text clustering. The main difficulty in the application of genetic algorithms (GAs) for document clustering is thousands or even tens of thousands of dimensions in feature space which is typical for textual data. Because the most straightforward and popular approach represents texts with the vector space model (VSM), that is, each unique term in the vocabulary represents one dimension. Latent semantic indexing (LSI) is a successful technology in information retrieval which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. Meanwhile, LSI takes into account the effects of synonymy and polysemy, which constructs a semantic structure in textual data. GA belongs to search techniques that can efficiently evolve the optimal solution in the reduced space. We propose a variable string length genetic algorithm which has been exploited for automatically evolving the proper number of clusters as well as providing near optimal data set clustering. GA can be used in conjunction with the reduced latent semantic structure and improve clustering efficiency and accuracy. The superiority of GAL approach over conventional GA applied in VSM model is demonstrated by providing good Reuter document clustering results.

A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters

In this paper, a new symmetry-based genetic clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Strings comprise both real numbers and the don't care symbol in order to encode a variable number of clusters. Here, assignment of points to different clusters are done based on a point symmetry (PS)-based distance rather than the Euclidean distance. A newly proposed PS-based cluster validity index, sym-index, is used as a measure of the validity of the corresponding partitioning. The algorithm is, therefore, able to detect both convex and nonconvex clusters irrespective of their sizes and shapes as long as they possess the symmetry property. Kd-tree-based nearest neighbor search is used to reduce the complexity of computing PS-based distance. A proof on the convergence property of variable string length genetic algorithm with PS- distance-based clustering (VGAPS-clustering) technique is also provided. The effectiveness of VGAPS-clustering compared to variable string length genetic K-means algorithm (GCUK-clustering) and one recently developed weighted sum validity function-based hybrid niching genetic algorithm (HNGA-clustering) is demonstrated for nine artificial and five real-life data sets.

ACTIVE SITE DRIVEN LIGAND DESIGN: AN EVOLUTIONARY APPROACH

An evolutionary approach for designing a ligand molecule that can bind to the active site of a target protein is described in this article. An earlier attempt in this regard assumed a fixed tree structure of the ligand on both sides of the pharmacophore, and used a genetic algorithm for optimizing the van der Waals energy. However, it is evident that knowledge about the size of the tree is difficult to obtain an a priori. Moreover, it will also change from one active site to another. This limitation is overcome in the present article by using variable string length genetic algorithm (VGA) for evolving an appropriate arrangement of the basic functional units of the molecule to be designed, whose size may now vary. The crossover and mutation operators are appropriately redesigned in order to tackle the concept of variable length chromosomes. Once the geometry of the molecule is obtained, the possible three-dimensional structure and its docking energy is determined. Results are demonstrated for five different target proteins both numerically and pictorially. It is found that not only does the molecule designed using variable length representation, in general, have lower energy values, the docking energies are also lower, as compared to the molecule evolved using fixed size representation.

A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification

In this article, the effectiveness of variable string length genetic algorithm along with a recently developed fuzzy cluster validity index ( PBMF) has been demonstrated for clustering a data set into an unknown number of clusters. The flexibility of a variable string length Genetic Algorithm (VGA) is utilized in conjunction with the fuzzy indices to determine the number of clusters present in a data set as well as a good fuzzy partition of the data for that number of clusters. A comparative study has been performed for different validity indices, namely, PBMF, XB, PE and PC. The results of the fuzzy VGA algorithm are compared with those obtained by the well known FCM algorithm which is applicable only when the number of clusters is fixed a priori. Moreover, another genetic clustering scheme, that also requires fixing the value of the number of clusters, is implemented. The effectiveness of the PBMF index as the optimization criterion along with a genetic fuzzy partitioning technique is demonstrated on a number of artificial and real data sets including a remote sensing image of the city of Kolkata.

Genetic Algorithms-Based Methodologies for Design Optimization of Trusses

Design optimization of trusses consists of arriving at optimum topology, configuration, and sizes of cross-sectional parameters, when weight of the truss is minimum, satisfying a set of specified constraints. Genetic algorithms-based methods provide ideal techniques for modeling many practical considerations, which make the optimal solution constructible. Two improved methods for size and configuration optimization and topology optimization are presented in this paper. The first one is a two-phase method, which in addition to arriving at better solutions compared to those obtained using earlier methods, also improves the computational efficiency. The second method, based on a variable string length genetic algorithm (VGA), addresses the topology optimization problem, taking into account a number of practical issues. Classical problems from literature are solved and the results are compared. Features of the proposed method, which help in modeling and application to optimal design of large truss structures,...