27587473
Generalized cycle crossover for graph partitioning
We propose a new crossover that generalizes cycle crossover to permutations with repetitions and naturally suits partition problems. We tested it on graph partitioning problems obtaining excellent results.
17681470
New Usage of Sammon's Mapping for Genetic Visualization
It is a hard problem to understand the fitness landscape of a problem as well as the evolution of genetic algorithms. For the purpose, we adopt Sammon's mapping for the investigation. We demonstrate its usefulness by applying it to the graph partitioning problem which is a well-known NP-hard problem. Also, through the investigation of schema traces, we explain the genetic process and the reordering effect in the genetic algorithm.
12076
Multi-attractor gene reordering for graph bisection
We propose a new gene reordering scheme for the graph bisection problem. Our gene reordering starts with two or more vertices to capture the clustering structure of graphs effectively. We devised a chromosome repairing method for hybrid genetic search, which helps exploit clusters when combined with gene reordering. Experimental tests showed that the suggested reordering scheme significantly improves the performance of genetic algorithms over previous reordering methods.
15089682
Lagrange Multiplier Method for Multi-campaign Assignment Problem
It is crucial to maximize marketing efficiency and customer satisfaction in personalized marketing. In this paper, we raise the multiple recommendation problem which occurs when performing several personalized campaigns simultaneously. We formulate the multi-campaign assignment problem to solve this issue and propose methods for solving the problem. The most notable element is the Lagrange multiplier method. Roughly speaking, Lagrange multiplier reduces problem complexity with a minor impact on optimality. However, it is not easy to find Lagrange multipliers in exact accord with problem constraints. We use a genetic algorithm for finding optimal Lagrange multipliers. Through the experiments, we verify the effectiveness of the problem formulation and our genetic approach.
36639292
Lock-Gain Based Graph Partitioning
We propose a new heuristic for the graph partitioning problem. Based on the traditional iterative improvement framework, the heuristic uses a new type of gain in selecting vertices to move between partitions. The new type of gain provides a good explanation for the performance difference of tie-breaking strategies in KL-based iterative improvement graph partitioning algorithms. The new heuristic performed excellently. Theoretical arguments supporting its efficacy are also provided. As the proposed heuristic is considered a good candidate for local optimization engines in metaheuristics, we combined it with a genetic algorithm as a sample case and obtained a surprising result that even the average results over 1,000 runs equalled the best known for most graphs.
19448510
A Genetic Approach for Gene Selection on Microarray Expression Data
Microarrays allow simultaneous measurement of the expression levels of thousands of genes in cells under different physiological or disease states. Because the number of genes exceeds the number of samples, class prediction on microarray expression data leads to an extreme “curse of dimensionality” problem. A principal goal of these studies is to identify a subset of informative genes for class prediction to reduce the curse of dimensionality. We propose a novel genetic approach that selects a subset of predictive genes for classification on the basis of gene expression data. Our genetic algorithm maximizes correlation between genes and classes and minimizes intercorrelation among genes. We tested the genetic algorithm on leukemia data sets and obtained improved results over previous results.
8330560
Shape Comparison of the Hippocampus Using a Multiresolution Representation and ICP Normalization
It is known that deformation of the hippocampus shape is involved with several neurological diseases. In this paper, we propose a hybrid shape representation scheme, which consists of multiresolution skeletons, voxels and meshes for the shape analysis of the hippocampus. Initially, a hippocampal surface model is reconstructed from MRI and then it is placed into a canonical coordinate system, where the position, orientation and scaling are normalized. From the voxel representation of the hippocampus, multiresolution skeletons are extracted and Iterative Closest Point normalization is carried out. Then the shape similarity of two hippocampal models is computed with a hierarchical fashion. In addition, we have implemented a neural network based classifier to discriminate whether a hippocampal model is normal or not. Results indicate that the proposed hybrid representation and the skeleton-based normalization using ICP are very effective in 3D shape analysis of the hippocampus.
2382558
Convex Hull Ensemble Machine for Regression and Classification
We propose a new ensemble algorithm called Convex Hull Ensemble Machine (CHEM). CHEM in Hilbert space is first developed and modified for regression and classification problems. We prove that the ensemble model converges to the optimal model in Hilbert space under regularity conditions. Empirical studies reveal that, for classification problems, CHEM has a prediction accuracy similar to that of boosting, but CHEM is much more robust with respect to output noise and never overfits datasets even when boosting does. For regression problems, CHEM is competitive with other ensemble methods such as gradient boosting and bagging.
33400437
Facial Expression Recognition Using Active Appearance Model
This paper describes a facial expression recognition system based upon Active Appearance Model (AAM), which has been typically used for the face recognition task. Given that AAM has been also used in tracking the moving object, we thought it could be effective in recognizing the facial expressions of humans. Our results show that the performance of the facial expression recognition using AAM is reliably high when it combined with an enhanced Fisher classification model.
6422061
A new branch and bound algorithm for minimizing mean tardiness in two-machine flowshops
Abstract We consider the two-machine flowshop scheduling problem with the objective of minimizing mean tardiness. Presented are several properties that are used to calculate lower bounds on total tardiness of jobs for a given partial sequence and to identify sequences dominated by others. We develop a branch and bound algorithm using these bounds and dominance rule. This algorithm is compared with an existing algorithm on randomly-generated test problems.
30845722
Accurate exploration of timing and area trade-offs in arithmetic optimization using carry-save-adders
Timing and area of circuits are two of the most important design criteria to be optimized in data path synthesis. In addition, carry-save-adder (CSA) has been proven to be one of the most efficient implementation units in optimizing timing and/or area of arithmetic circuits. However, the existing approaches are restricted in using CSAs, i.e., optimizing operation trees separately without any interaction between them, resulting in a locally optimized CSA circuit. To overcome this limitation, we propose a practically efficient solution to the problem of an accurate exploration of timing and area trade-offs in optimizing arithmetic circuits in the presence of multiple operation trees using CSAs. The application of our approach is able to find a best CSA implementation of circuit in terms of timing and area.