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Computational intelligence is an ongoing area of research, which has been successfully utilized in the analysis and modeling of the tremendous amount of biological data accumulated under different high throughput genome sequencing projects. The data gathered is mainly comprised of DNA, RNA and protein sequences, which are imprecise, incomplete and increasing exponentially. Classification of protein...
In this paper, we present a SVM multi-classification decision-tree optimization algorithm based on genetic algorithm (GA) in order to overcome the defect of the error accumulation which is caused by the fixed tree configuration of traditional support vector machine (SVM) multi-classification decision-tree algorithms and the random positions of their decision nodes. We adopt the “classification margin”...
In this paper, an improved Genetic Algorithm is proposed. We introduce Support Vector Machines (SVM) into Genetic Algorithm (GA) based on the traditional GA. Through forecasting the gene encoding to divide the chromosomes, it reduces the searching range of gene. Finally, the experimental results prove that the improved algorithm has reached the aim of accelerating the constringency speed and shortening...
Feature selection is a hot topic in current information science, especially in the field of pattern recognition. In this paper, a combination feature selection Algorithm, ReGA, which merges the feature selection technique, ReliefF, into Genetic Algorithms Method, is presented. Experiments show that the new method improves the fitness of initial population, it can find the optimal solution more quickly,...
Classifier fusion is considered as one of the best strategies for improving performance of general purpose classification systems. On the other hand, fusion strategy space strongly depends on classifiers, features and data spaces. As the cardinality of this space is exponential, one needs to resort to a heuristic to find a sub-optimal fusion strategy. In this work, we present a new adaptive feature...
With the advent of large-scale high density single nucleotide polymorphism (SNP) arrays, case-control association studies have been performed to identify predisposing genetic factors that influence many common complex diseases. These genotyping platforms provide very dense SNP coverage per one chip. Much research has been focusing on multivariate genetic model to identify genes that can predict the...
Text Categorization is an important research branch in the data mining domain. In this paper, an improved Naive Bayesian Classifier which is based on the Genetic Algorithms is proposed. It can make an effective Naive Bayesian classifier with excellent attributes Set in the field of text categorization. The experiments show that this method has a good classification performance.
A major problem with text classification problems is the high dimensionality of the feature space. This paper investigates how genetic algorithm and k-means algorithm can help select relevant features in text classification. which uses the genetic algorithm (GA) optimization features to implement global searching, and uses k-means algorithm to selection operation to control the scope of the search,...
This paper transformed the process of Chinese question answering into agent coalition formation first, and then got the solution by using of combination of genetic algorithm and ant colony algorithm. The idea and routine of the algorithm were given. Coding scheme, selecting scheme, crossover operator, mutation operator and so on of genetic algorithm which suitable for Chinese question answering agent...
This paper proposes an effective clustering algorithm for databases, which are benchmark data sets of data mining applications. We present a Genetic Clustering Algorithm (GCA) that finds a globally optimal partition of a given data sets into a specified number of clusters. The algorithm is distance-based and creates centroids. To evaluate the proposed algorithm, we use some artificial data sets and...
A novel algorithm, the k-means clustering algorithm based on immune genetic algorithm (KMCIGA) is put forward. To improve the Genetic operators, the conception of concentration in the immune algorithm and the dynamic chromosome coding are used. Strategies and methods of selecting vaccines and constructing an immune operator are also given. KMCIGA is illustrated to be obviously better than the traditional...
A fuzzy classification system is constructed based on quantum genetic algorithm (QGA) and fuzzy theory. Firstly, fuzzy rules are generated from numerical data for classification problems, in which number axis is fuzzy partitioned with trapezoid method. Second, it uses QGA to select significant fuzzy rules and removes unnecessary rules, so fuzzy rules reach an optimization state. Finally, the feasibility...
Neural network often is trained by multilayer feedforward neural network ago, but it may fall into local minimum point. In this article, swarm optimization particle is improved so that it can adapt to solve optimization problem of discrete variables. At the same time, introducing the crossover operation of genetic algorithm make it form hybrid particle swarm optimization. Then combining the method...
Decision tree is mainly used in classification and predictive model. The introduction of generalized decision tree (GDT) realized the unification of classification rules and decision tree structure. Meanwhile, a new method that based on DNA coding genetic algorithm to construct decision tree was proposed. It firstly classified dataset by C4.5 to get initial rule sets, then optimized the rule sets...
High-dimensional data clustering is an open problem in modern data mining. This paper proposed a new genetic algorithm-based feature selection for high-dimensional data clustering, called GA-FSFclustering. This approach searches effective feature subsets for clustering in all features by genetic algorithm. The candidate features and cluster centers are real number encoded. A new criterion for evaluating...
In this paper we propose a novel framework for the multi-objective optimization of a video codec based on genetic algorithms. The proposed framework is designed to jointly minimize the complexity, memory usage (both at the encoder and decoder), bit rate and to maximize the quality of the compressed video stream. In particular, in our present attempt the optimization strategy is designed to determine...
A genetic algorithm-based high-dimensional data clustering technique, called GA-HDclustering, is proposed in this paper. This approach searches feature subspace by genetic algorithms to find the effective clustering feature subspaces. The candidate features and cluster centers are binary encoded, and the degree of feature subspace contributes to subspace clustering is proposed as the fitness function...
Firstly, the paper makes a briefly analysis and comment about the fuzzy c-means clustering algorithm. Then a new kind of hybrid genetic algorithm is proposed on the base of the combination of genetic algorithm and simulated annealing algorithm, and it is applied in fuzzy c-means clustering. It overcomes the locality and the Sensitivity to initial clustering central of fuzzy c-means clustering, by...
We present a noble encoding method for designing an optimal fuzzy classifier with evolutionary optimization. Evolutionary designs of fuzzy classifiers is divided into design of fuzzy rules and design of fuzzy membership functions. Among these design problems, for an evolutionary design of membership functions, the shapes of each membership function are mainly considered in the previous related works...
This paper proposes a new strategy for solving the service restoration problem in large-scale distribution systems (DS). Due to the presence of various conflicting objective functions and constraints, the service restoration task is a multi-objective, multi-constraint optimization problem. As a consequence, finding feasible solutions is a hard task. The proposed strategy uses a new tree encoding,...
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