7572098
New Usage of SOM for Genetic Algorithms
Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantizations, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.
16116503
Evolutionary programming techniques for constrained optimization problems
Two evolutionary programming (EP) methods are proposed for handling nonlinear constrained optimization problems. The first, a hybrid EP, is useful when addressing heavily constrained optimization problems both in terms of computational efficiency and solution accuracy. But this method offers an exact solution only if both the mathematical form of the objective function to be minimized/maximized and its gradient are known. The second method, a two-phase EP (TPEP) removes these restrictions. The first phase uses the standard EP, while an EP formulation of the augmented Lagrangian method is employed in the second phase. Through the use of Lagrange multipliers and by gradually placing emphasis on violated constraints in the objective function whenever the best solution does not fulfill the constraints, the trial solutions are driven to the optimal point where all constraints are satisfied. Simulations indicate that the TPEP achieves an exact global solution without gradient information, with less computation time than the other optimization methods studied here, for general constrained optimization problems.
5817098
Neural Network Normalization for Genetic Search
An arbitrary neural network has a number of functionally equivalent other networks. This causes redundancy in genetic representation of neural networks, which considerably undermines the merit of crossover in GAs [1]. This problem has received considerable attention in the past and has also been called the “competing conventions” problem [2].
39277510
Lagrangian-Based Evolutionary Programming for Constrained Optimization
In this paper, Lagrangian-based evolutionary programming, Evolian is proposed for the general constrained optimization problem, which incorporates the concept of (1) multi-phase optimization process and (2) constraint scaling techniques to resolve the ill-conditioning problem. In each phase of Evolian, the typical EP is performed using augmented Lagrangian objective function with a penalty parameter fixed. If there is no improvement in the best objective function in one phase, another phase is performed after scaling the constraints, and updating the Lagrange multipliers and penalty parameter. Simulation results indicate that Evolian gives outperforming or at least reasonable solutions for multivariable heavily constrained optimization problem compared to other several evolutionary computation based methods.
1268203
Quantum-Inspired Evolutionary Algorithm-Based Face Verification
Face verification is considered to be the main part of the face detection system. To detect human faces in images, face candidates are extracted and face verification is performed. This paper proposes a new face verification algorithm using Quantum-inspired Evolutionary Algorithm (QEA). The proposed verification system is based on Principal Components Analysis (PCA). Although PCA related algorithms have shown outstanding performance, the problem lies in the selection of eigenvectors. They may not be the optimal ones for representing the face features. Moreover, a threshold value should be selected properly considering the verification rate and false alarm rate. To solve these problems, QEA is employed to find out the optimal distance measure under the predetermined threshold value which distinguishes between face images and non-face images. The proposed verification system is tested on the AR face database and the results are compared with the previous works to show the improvement in performance.
9661446
Normalization for neural network in genetic search
Jung-Hwan Kim School of Computer Science and Engineering Seoul National University Shilim-dong, Kwanak-gu, Seoul, 151-742 Korea aram@soar.snu.ac.kr Sung-Soon Choi School of Computer Science and Engineering Seoul National University Shilim-dong, Kwanak-gu, Seoul, 151-742 Korea sschoi@soar.snu.ac.kr Byung-Ro Moon School of Computer Science and Engineering Seoul National University Shilim-dong, Kwanak-gu, Seoul, 151-742 Korea moon@soar.snu.ac.kr
12688706
Unsupervised Learning from Complex Data: The Matrix Incision Tree Algorithm
Analysis of large-scale gene expression data requires novel methods for knowledge discovery and predictive model building as well as clustering. Organizing data into meaningful structures is one of the most fundamental modes of learning. DNA microarray data set can be viewed as a set of mutually associated genes in a high-dimensional space. This paper describes a novel method to organize a complex high-dimensional space into successive lower-dimensional spaces based on the geometric properties of the data structure in the absence of a priori knowledge. The matrix incision tree algorithm reveals the hierarchical structural organization of observed data by determining the successive hyperplanes that 'optimally' separate the data hyperspace. The algorithm was tested against published data sets yielding promising results.
10913816
A Web Services-enabled marketplace architecture for negotiation process management
As the eBusiness environment becomes more pervasive and dynamic, negotiations between companies are required more frequently than ever. Despite its potential value and the progress in research, the adoption of negotiation systems has been slow in practice. We believe one reason for this is insufficient consideration of process management aspects such as process design, description, and deployment. Business negotiations must be approached from the process management perspective since they take place in the context of corporate processes such as procurement or sales.In this paper, we study system support and automation of business-to-business (B2B) negotiations from the process management perspective. We propose a Web Services-enabled marketplace architecture for negotiation process management and refine it by adding pattern-based process composition. We validate the concept by implementing the proposed architecture using BPEL4WS and evaluating it from various perspectives.
754081
Flattened butterfly: a cost-efficient topology for high-radix networks
Increasing integrated-circuit pin bandwidth has motivateda corresponding increase in the degree or radix of interconnection networksand their routers. This paper introduces the flattened butterfly, a cost-efficient topology for high-radix networks. On benign (load-balanced) traffic, the flattened butterfly approaches the cost/performance of a butterfly network and has roughly half the cost of a comparable performance Clos network.The advantage over the Clos is achieved by eliminating redundant hopswhen they are not needed for load balance. On adversarial traffic, the flattened butterfly matches the cost/performance of a folded-Clos network and provides an order of magnitude better performance than a conventional butterfly.In this case, global adaptive routing is used to switchthe flattened butterfly from minimal to non-minimal routing - usingredundant hops only when they are needed. Minimal and non-minimal, oblivious and adaptive routing algorithms are evaluated on the flattened butterfly.We show that load-balancing adversarial traffic requires non-minimalglobally-adaptive routing and show that sequential allocators are required to avoid transient load imbalance when using adaptive routing algorithms.We also compare the cost of the flattened butterfly to folded-Clos, hypercube,and butterfly networks with identical capacityand show that the flattened butterfly is more cost-efficient thanfolded-Clos and hypercube topologies.
488943
A Combinatorial Toolbox for Protein Sequence Design and Landscape Analysis in the Grand Canonical Model
In modern biology, one of the most important research problems is to understand how protein sequences fold into their native 3D structures. To investigate this problem at a high level, one wishes to analyze the protein landscapes, i.e., the structures of the space of all protein sequences and their native 3D structures. Perhaps the most basic computational problem at this level is to take a target 3D structure as input and design a fittest protein sequence with respect to one or more fitness functions of the target 3D structure. We develop a toolbox of combinatorial techniques for protein landscape analysis in the Grand Canonical model of Sun, Brem, Chan, and Dill. The toolbox is based on linear programming, network flow, and a linear-size representation of all minimum cuts of a network. It not only substantially expands the network flow technique for protein sequence design in Kleinberg's seminal work but also is applicable to a considerably broader collection of computational problems than those considered by Kleinberg. We have used this toolbox to obtain a number of efficient algorithms and hardness results. We have further used the algorithms to analyze 3D structures drawn from the Protein Data Bank and have discovered some novel relationships between such native 3D structures and the Grand Canonical model.
6280619
Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machine
Predicting the destination of a protein in a cell is important for annotating the function of the protein. Recent advances have allowed us to develop more accurate methods for predicting the subcellular localization of proteins. One of the most important factors for improving the accuracy of these methods is related to the introduction of new useful features for protein sequences. In this paper we present a new method for extracting appropriate features from the sequence data by computing pairwise sequence alignment scores. As a classifier, support vector machine (SVM) is used. The overall prediction accuracy evaluated by the jackknife validation technique reached 94.70% for the eukaryotic non-plant data set and 92.10% for the eukaryotic plant data set, which is the highest prediction accuracy among the methods reported so far with such data sets. Our experimental results confirm that our feature extraction method based on pairwise sequence alignment is useful for this classification problem.