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A new genetic algorithm based on the theory of lamarckian evolution (Lam-GA) to solve multi-objective transportation optimization problem(MOTP) is presented in the paper. The algorithm carries through some local mutation according to certain rules after distributing transportation counts on the fuzzy rule basis, which can increase the intensity for searching better solution. Experimental data shows...
Based on the genetic algorithm for solving multi-objective optimization easily leads to the defect of premature and slow convergence, so an improved niche genetic algorithm is proposed. This algorithm is to select distance parameter equals to the minimum Euclidean distance between the best individuals, using the method of allele comparison to determine within the distance parameter individuals whether...
An improved genetic algorithm based on the hJ1 triangulation is proposed for optimization of dual multimodal function. With this algorithm, the optimal problems converse to solution of fixed point problems. The minimum points can be distinguished by using the Hessian Matrix. The test results of many typical functions indicated that the algorithm is valid and highly effective.
A novel memetic algorithm (MA) for the design of vector quantizers (VQs) is presented in this paper. The algorithm uses steady-state genetic algorithm (GA) for the global search and C-means algorithm for the local improvement. As compared with the usual MA using the generational GA for global search, the proposed MA effectively reduces the computational time for VQ training. In addition, it attains...
For multi-objective optimization problems, we introduced IPAGA (Improved Parallel Adaptive Genetic Algorithm) in this paper, a new parallel genetic algorithm which is based on Pareto Front. In this Algorithm, the non-dominated-set is constructed by the method of exclusion. The evolution population adopts the adaptive-crossover and adaptive-mutation probability, which can adjust the search scope according...
Danger Model Immune Algorithm(DMIA) is an algorithm based on the danger theory of biological immune system. In the basic algorithm, the danger area is fixed through the initial setting. It is an important parameter which will affect the capability of algorithm. In this paper, propose an adaptive danger area DMIA. The radius of danger area is decrease gradually according to the iteration steps. The...
A novel recursive scheme to compute the global and robust optimal variable fractional delay (VFD) filters based on the Particle Swarm Optimization (PSO) is developed in this paper. If the PSO is directly used to compute an optimal VFD filter the particles with high dimension might be yielded, which could require a long convergence time. Our recursive scheme invokes only the particles with much smaller...
This paper presents an algorithm for thermal optimization formulation strategies for multi-heat generation of integrated circuit (IC) on printed circuit board (PCB). Weighted-sum approach for multi-objective genetic algorithm (WMOGA) with formulated initial placement and multi-constraints parameters (FIPMCP) are presented. FIPMCP is used for the components selection and components to PCB placement...
Due to the importance of power/ground network, lots of researches have been made on it. But they only focused on the minimal area of it. By discussion on the relation among Vdd, performance and power consumption, this paper proposes an optimal algorithm using GA and SLP method where area, performance and power consumption can be simultaneously evaluated. As a result, the power/ground network is designed...
A new adaptive real-coded memetic algorithm has been developed for continuous optimization problems. The proposed algorithm utilizes an adaptive variant of continuous ant colony system for local search. Here new adaptive strategies are utilized for online tuning of the number of local search steps and the width of the search interval over each dimension of the search space. A new crossover scheme...
The blind signal extraction (BSE) approach based on an on-line predictor is put forward and developed recently. All the on-line predictor extraction algorithms are optimizing process based on gradient algorithm at present. The choice of initial values and learning rates could influence the performance of the algorithm. A kind of BSE approach based on genetic algorithm is put forward in the paper....
This paper presents a simultaneous optimization method of a case-based reasoning (CBR) system using a genetic algorithm (GA) for product designing. Prior research proposed many hybrid models of CBR and the GA for product design. However, those models were not good at aiding at creative designing. In this study, CBR and GA combine with an algorithm process for simulation of directed similarity association...
Equiangular tight frames have applications in communications, signal processing, and coding theory. Previous work demonstrates that few real equiangular tight frames exist for most pairs (n,d), where the frame ??n,d is a d ?? n matrix with d ?? n. This work proposes a genetic algorithm as a solution to the frame design problem. Specifically, the problem of designing real equiangular tight frames by...
A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions automatically, based on the primitive features, for an image pattern recognition system on the diagnosis of the disease OPMD. Prior to the feature function generation, we introduce a novel technique of the primitive texture feature extraction, which...
In order to improve the optimization of middle and large-scale logic circuits, a heuristic inspired traversal method of circuit polarities called the least operation traversal method (LOTM) is proposed. Firstly, the polarity traversal sequence problem of fixed-polarity RM circuits is analyzed and a mathematical model of the problem is given and discussed; Secondly, the detailed realization of the...
The SET k-cover problem is an NP-complete combinatorial optimization problem, which is derived from constructing energy efficient wireless sensor networks (WSNs). The goal of the problem is to find a way to divide sensors into disjoint cover sets, with every cover set being able to fully cover an area and the number of cover sets maximized. Instead of using deterministic algorithms or simple genetic...
This paper discussed the types of Assembly Line Balancing Problem and relevant algorithms, presented the mathematical description of Assembly Line Balancing Problem-2. Using PSO's global search capability and high efficient of searching with SA's local search capability, proposing a hybrid PSO algorithm for Assembly Line Balancing Problem-2 and giving out a solving procedure based on the analysis...
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...
Genetic algorithm is an important algorithm of association rule mining. However, there is some issues that genetic algorithm easy to lead prematuring convergence and into the plight of local optimum, or convergence too much time and consume a large amount of time to search. For resolving this issues, the paper improves the algorithm through adopting an adaptive mutation rate and improving the methods...
Three evolutionary computing algorithms are applied to a constrained parameter, combinatorial optimization problem; the Sudoku puzzle. These methodologies include, quantum simulated annealing, cultural genetic algorithm and a hybrid between simulated annealing and genetic algorithm. The results obtained from these techniques indicate that the most effective of these optimization techniques is quantum...
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