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The commercial banks need identify exceptional client in their large number of customers to prevent abnormal customer's risk. In this paper, four types of abnormal data detection method is introduced, present a new method- the k-medoids clustering algorithm combining genetic algorithm to detect the outlier. Finally, apply the algorithm to analysis credit data sets, detect outlier and identify abnormal...
Association rule mining based on support and confidence generates a large number of rules. However, post analysis is required to obtain interesting rules as many of the generated rules are useless. We pose mining association rules as multi-objective optimization problem where objective functions are rule interestingness measures and use NSGA-II, a well known multi-objective evolutionary algorithm...
Differential evolution (DE) algorithm is a heuristic approach that gains more interest in today's research. It finds the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithm using similar operators; crossover, mutation and selection. This paper addresses the restrictive...
In order to save the consumption in terms of instruction and storage, to simplify the code, and to enhance the coding quality, this paper, based on the architecture design of the stereo encoder, has analyzed the arranging relationship between the channel and the block in each frame and has optimized the data process and the looping mode of the current encoder. In addition, the start points and the...
For nonlinear bi-level programming problems in which the follower's problem is linear, the paper develops a genetic algorithm based on a mixed encoding technique. At first, each individual consists of two parts, the first part is the leader's variable values using real-encoding, whereas the second one is the sequence number of basic variables of the follower's programming, which are some integers...
The location optimization model which is by established wireless sensor networks is a multi-objective and multi-constrained non-linear equation; and genetic algorithm as a evolutionary algorithm, it has merit of simple condition in application, strong ability in search capability, and particularly suitable for multi-objective, multi-binding solution, so it is very suitable for wireless sensor network...
Knapsack problem is applied broadly to practice in resource allocation, investment decision-making, storage allocation, loading problem and so on. The paper adopts Handel-C language to program for the simple and improved genetic algorithm that solve knapsack problem. The procedures of the two algorithms are provided in detail in the paper. The improved genetic algorithm enhances obviously global search...
A novel algorithm called free search (FS) is applied to solve the scheduling problems on a single batch processing machine with non-identical job sizes (NSBM) in this paper. The concept of sensibility was introduced in FS, with which the algorithm had no restriction of the probability to zero for the parts of the search space and thus it could avoid premature convergence. In order to make it feasible...
Model to minimize the objective function of the annual reduced cost with the constraints of hydraulic conditions in annular pipe water supply engineering is given in this paper and the genetic algorithm is applied to the actual question. The quick velocity update strategy of particle swarm optimization algorithm is used to modify the GApsilas evolutionary strategy. The advanced algorithm is tested...
One hybrid intelligent algorithm is designed to solve the annular water supply network optimization. The model to minimize the objective function of the annual reduced cost with the constraints of hydraulic conditions. The intelligent optimization algorithm population based incremental learning - PBIL based on probability learning strategy is combined to particle swarm optimization algorithm-PSO....
This paper proposes a novel algorithm for signal classification problems. We consider a non-stationary random signal, where samples can be classified into several different classes, and samples in each class are identically independently distributed with an unknown probability distribution. The problem to be solved is to estimate the probability distributions of the classes and the correct membership...
In this paper an improved genetic algorithm is proposed to solve optimal problems applying fixed point algorithms of continuous self-mapping in Euclidean space. The algorithm operates on subdivision of searching space and generates the integer labels at the vertices, and then only mutation operator relying on the genetic encoding designed which is proposed by virtue of the concept of relative coordinates...
Computational protein design can be formulated as an optimization problem, where the objective is to identify the sequence of amino acids that minimizes the energy of a given protein structure. In this paper, we propose a novel search-based approach that utilizes a Boolean function to encode the solution space where the function's onset represents the sequences considered during the search. We first...
The computational costs of Cauchy Reed-Solomon (CRS) encoding operation make a great impact on the performance of its practical applications. The letter concentrates on how to construct a good Cauchy matrix which can lead to an efficient CRS coding scheme. We first formally model the problem by using a binary quadratic programming, then present an approximate method called localized greedy algorithm...
In order to improve some fundamental problems of the clonal selection algorithm (CSA), a novel clonal selection algorithm (NCSA) is proposed. After analyzing the mechanism of the clonal selection and proposing the antibody model, the basic character of the application problem fused into the NCSA based on rearrangements of antibody molecule coding genes. Next, we analyzed synthetically the antibody-antigen...
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