6826338
Geometric crossover for multiway graph partitioning
Geometric crossover is a representation-independent generalization of the traditional crossover defined using the distance of the solution space. Using a distance tailored to the problem at hand, the formal definition of geometric crossover allows to design new problem-specific crossovers that embed problem-knowledge in the search. The standard encoding for multiway graph partitioning is highly redundant: each solution has a number of representations, one for each way of labeling the represented partition. Traditional crossover does not perform well on redundant encodings. We propose a new geometric crossover for graph partitioning based on a labeling-independent distance that filters the redundancy of the encoding. A correlation analysis of the fitness landscape based on this distance shows that it is well suited to graph partitioning. Our new genetic algorithm outperforms existing ones.
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.
10542004
Investigation of the Fitness Landscapes and Multi-parent Crossover for Graph Bipartitioning
An empirical study is performed on the local-optimum space of graph bipartitioning. We examine some statistical features of the fitness landscape. They include the cost-distance correlations and the properties around the central area of local optima. The study revealed some new notable results about the properties of the fitness landscape; e.g., the central area yielded fairly good quality in the local-optimum space. We performed an experiment on a spectrum of different exploitation strengths of the central areas. From the results, it seems attractive to exploit the central area, but excessive or insufficient exploitation is not desirable.
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.
3094791
Multicampaign assignment problem
It is crucial to maximize targeting efficiency and customer satisfaction in personalized marketing. State-of-the-art techniques for targeting focus on the optimization of individual campaigns. Our motivation is the belief that the effectiveness of a campaign with respect to a customer is affected by how many precedent campaigns have been recently delivered to the customer. We raise the multiple recommendation problem, which occurs when performing several personalized campaigns simultaneously. We formulate the multicampaign assignment problem to solve this issue and propose algorithms for the problem. The algorithms include dynamic programming and efficient heuristic methods. We verify by experiments the effectiveness of the problem formulation and the proposed algorithms.
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.
206667073
Fault-diagnosis for a class of distributed control multistage interconnection networks
A class of (2 log/sub 2/ N-1)-stage interconnection networks is suitable for fault-tolerance with effective use of the redundant paths which provide multiple disjoint paths between each pair of input/output terminals. This paper focuses on a new fault-diagnosis method for a class of distributed control (2 log/sub 2/ N-1)-stage interconnection networks. To perform this fault-diagnosis method, the self-routing tags based on a given multistage interconnection network are generated for each phase. Six tests with self-routing tags can detect a faulty switching element and a link stuck fault in a (2 log/sub 2/ N-1)-stage interconnection network. Regarding the fault location problem, the fault-location procedure to use O(log/sub 2/ log 2N) tests for a distributed control network is proposed.
21112002
A backward approach in list scheduling algorithms for multi-machine tardiness problems
Abstract List scheduling algorithms are commonly used for multi-machine scheduling problems, especially for those with precedence relationships among operations. In most list scheduling algorithms, operations are scheduled forward. In this paper, we suggest a backward approach in which the operations are scheduled backward. We discuss some details that must be considered when applying the backward approach to problems with due dates and suggest several methods of defining parameters needed in the backward approach. Performance of this approach is tested on job shop problems and identical machine problems with several types of precedence constaints.
36946020
Face Representation Method Using Pixel-to-Vertex Map (PVM) for 3D Model Based Face Recognition
3D model based approach for face recognition has been spotlighted as a robust solution under variant conditions of pose and illumination. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time. In this paper, we propose a novel 3D face representation algorithm to reduce the number of vertices and optimize its computation time. Finally, we evaluate the performance of proposed algorithm with the Korean face database collected using a stereo-camera based 3D face capturing device.
205099619
Simulated annealing and genetic algorithms for scheduling products with multi-level product structure
We consider a short-term production scheduling problem in a manufacturing system producing products with multi-level product structure, and their components and subassemblies. The problem is to schedule production of components, subassemblies, and final products with the objective of minimizing the weighted sum of tardiness and earliness of the items. We consider due dates of final products, precedence relationships among items, and processing capacity of the manufacturing system. The scheduling problem is solved with simulated annealing and genetic algorithms, which are search heuristics often used for global optimization in a complex search space. To compare the performance of these algorithms with the finite loading method, which is often used in practice, computational experiments are carried out using randomly generated test problems and results are reported.
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.