Zne-Jung Lee

Particle swarm optimization for parameter determination and feature selection of support vector machines

Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, without reducing SVM classification accuracy. A particle swarm optimization (PSO) based approach for parameter determination and feature selection of the SVM, termed PSO+SVM, is developed. Several public datasets are employed to calculate the classification accuracy rate in order to evaluate the developed PSO+SVM approach. The developed approach was compared with grid search, which is a conventional method of searching parameter values, and other approaches. Experimental results demonstrate that the classification accuracy rates of the developed approach surpass those of grid search and many other approaches, and that the developed PSO+SVM approach has a similar result to GA+SVM. Therefore, the PSO+SVM approach is valuable for parameter determination and feature selection in an SVM.

Parameter determination of support vector machine and feature selection using simulated annealing approach

Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM. To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM.

Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics

A general weapon-target assignment (WTA) problem is to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force asset. Genetic algorithms (GAs) are widely used for solving complicated optimization problems, such as WTA problems. In this paper, a novel GA with greedy eugenics is proposed. Eugenics is a process of improving the quality of offspring. The proposed algorithm is to enhance the performance of GAs by introducing a greedy reformation scheme so as to have locally optimal offspring. This algorithm is successfully applied to general WTA problems. From our simulations for those tested problems, the proposed algorithm has the best performance when compared to other existing search algorithms.

Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment

Multiple sequence alignment, known as NP-complete problem, is among the most important and challenging tasks in computational biology. For multiple sequence alignment, it is difficult to solve this type of problems directly and always results in exponential complexity. In this paper, we present a novel algorithm of genetic algorithm with ant colony optimization for multiple sequence alignment. The proposed GA-ACO algorithm is to enhance the performance of genetic algorithm (GA) by incorporating local search, ant colony optimization (ACO), for multiple sequence alignment. In the proposed GA-ACO algorithm, genetic algorithm is conducted to provide the diversity of alignments. Thereafter, ant colony optimization is performed to move out of local optima. From simulation results, it is shown that the proposed GA-ACO algorithm has superior performance when compared to other existing algorithms.

A hybrid search algorithm with heuristics for resource allocation problem

The resource allocation problelm is to allocate resources to activities so that the cost becomes as optimal as possible. In this paper, a hybrid search algorithm with heuristics for resource allocation problem encountered in practice is proposed. The proposed algorithm has both the advantages of genetic algorithm (GA) and ant colony optimization (ACO) that can explore the search space and exploit the best solution. In our implelmentation, both GA and ACO are well designed for the resource allocation problelm. Fur thermore, heuristics are used to ameliorate the search performance for resource allocation problem. Simulation results were reported and the proposed algorithm indeed have admirable performance for tested problems.

Applying hybrid meta-heuristics for capacitated vehicle routing problem

The capacitated vehicle routing problem (CVRP) is one of the most important problems in the optimization of distribution networks. The objective of CVRP, known demands on the cost of originating and terminating at a delivery depot, is to determine the optimal set of routes for a set of vehicles to deliver customers. CVRP is known to be NP-hard problem, and then it is difficult to solve this problem directly when the problem size is large. In this paper, a hybrid algorithm of simulated annealing and tabu search is applied to solve CVRP. It takes the advantages of simulated annealing and tabu search for solving CVRP. Simulation results are reported on classical fourteen instances and twenty large-scale benchmark instances. From simulation results, the proposed algorithm finds eight best solutions of classical fourteen instances. Additionally, the solutions of the proposed algorithm have also admirable performance for twenty large-scale benchmark instances. It shows that the proposed algorithm is competitive with other existing algorithms for solving CVRP.

A hybrid watermarking technique applied to digital images

In this paper, a hybrid watermarking technique applied to digital images is proposed. A watermarking technique is to insert copyright information into digital images that the ownerships can be declared. A fundamental problem for embedding watermarks is that the ways of pursuing transparency and robustness are always trade-off. To solve this problem, a hybrid watermarking technique is proposed to improve the similarity of extracted watermarks. In the proposed technique, the parameters of perceptual lossless ratio (PLR) for two complementary watermark modulations are first derived. Furthermore, a hybrid algorithm based on genetic algorithm (GA) and particle swarm optimization (PSO) is simultaneously performed to find the optimal values of PLR instead of heuristics. From simulation results, it shows the superiority of the proposed hybrid watermarking technique for digital images.

An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer

OBJECTIVE The type of data in microarray provides unprecedented amount of data. A typical microarray data of ovarian cancer consists of the expressions of tens of thousands of genes on a genomic scale, and there is no systematic procedure to analyze this information instantaneously. To avoid higher computational complexity, it needs to select the most likely differentially expressed gene markers to explain the effects of ovarian cancer. Traditionally, gene markers are selected by ranking genes according to statistics or machine learning algorithms. In this paper, an integrated algorithm is derived for gene selection and classification in microarray data of ovarian cancer. METHODS First, regression analysis is applied to find target genes. Genetic algorithm (GA), particle swarm optimization (PSO), support vector machine (SVM), and analysis of variance (ANOVA) are hybridized to select gene markers from target genes. Finally, the improved fuzzy model is applied to classify cancer tissues. RESULTS The microarray data of ovarian cancer, obtained from China Medical University Hospital, is used to test the performance of the proposed algorithm. In simulation, 200 target genes are obtained after regression analysis and six gene markers are selected from the hybrid process of GA, PCO, SVM and ANOVA. Additionally, these gene markers are used to classify cancer tissues. CONCLUSIONS The proposed algorithm can be used to analyze gene expressions and has superior performance in microarray data of ovarian cancer, and it can be performed on other studies for cancer diagnosis.

A GENETIC ALGORITHM WITH DOMAIN KNOWLEDGE FOR WEAPON-TARGET ASSIGNMENT PROBLEMS

Abstract In this paper, a novel genetic algorithm, including domain specific knowledge into the crossover operator and the local search mechanism for solving weapon‐target assignment (WTA) problems is proposed. The WTA problem is a full assignment of weapons to hostile targets with the objective of minimizing the expected damage value to own‐force assets. It is an NP‐complete problem. In our study, a greedy reformation and a new crossover operator are proposed to improve the search efficiency. The proposed algorithm outperforms its competitors on all test problems.

Robust and fast learning for fuzzy cerebellar model articulation controllers

In this paper, the online learning capability and the robust property for the learning algorithms of cerebellar model articulation controllers (CMAC) are discussed. Both the traditional CMAC and fuzzy CMAC are considered. In the study, we find a way of embedding the idea of M-estimators into the CMAC learning algorithms to provide the robust property against outliers existing in training data. An annealing schedule is also adopted for the learning constant to fulfil robust learning. In the study, we also extend our previous work of adopting the credit assignment idea into CMAC learning to provide fast learning for fuzzy CMAC. From demonstrated examples, it is clearly evident that the proposed algorithm indeed has faster and more robust learning. In our study, we then employ the proposed CMAC for an online learning control scheme used in the literature. In the implementation, we also propose to use a tuning parameter instead of a fixed constant to achieve both online learning and fine-tuning effects. The simulation results indeed show the effectiveness of the proposed approaches.