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2009 | Book

Index Fund Optimization Using Genetic Algorithm and Scatter Diagram Based on Coefficients of Determination Read chapter Read first chapter

Intelligent and Evolutionary Systems

Editors: Mitsuo Gen, David Green, Osamu Katai, Bob McKay, Akira Namatame, Ruhul A. Sarker, Byoung-Tak Zhang

Publisher: Springer Berlin Heidelberg

Book Series : Studies in Computational Intelligence

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

Artificial evolutionary systems are computer systems, inspired by ideas from natural evolution and related phenomena. The field has a long history, dating back to the earliest days of computer science, but it has only become an established scientific and engineering discipline since the 1990s, with packages for the commonest form, genetic algorithms, now widely available.

Researchers in the Asia-Pacific region have participated strongly in the development of evolutionary systems, with a particular emphasis on the evolution of intelligent solutions to highly complex problems. The Asia-Pacific Symposia on Intelligent and Evolutionary Systems have been an important contributor to this growth in impact, since 1997 providing an annual forum for exchange and dissemination of ideas. Participants come primarily from East Asia and the Western Pacific, but contributions are welcomed from around the World.

This volume features a selection of fourteen of the best papers from recent APSIES. They illustrate the breadth of research in the region, with applications ranging from business to medicine, from network optimization to the promotion of innovation.

Table of Contents

Frontmatter
Index Fund Optimization Using Genetic Algorithm and Scatter Diagram Based on Coefficients of Determination
Abstract
Index fund optimization is one of portfolio optimizations and can be viewed as a combinatorial optimization for portfolio managements. It is well known that an index fund consisting of stocks of listed companies on a stock market is very useful for hedge trading if the total return rate of a fund follows a similar path to the rate of change of a market index. In this paper, we propose a method that consists of a genetic algorithm and a heuristic local search on scatter diagrams to make linear association between the return rates and the rates of change strong. A coefficient of determination is adopted as a linear association measure of how the return rates follow the rates of change. We then apply the method to the Tokyo Stock Exchange. The results show that the method is effective for the index fund optimization.
Yukiko Orito, Manabu Takeda, Hisashi Yamamoto
Mining Bayesian Networks from Direct Marketing Databases with Missing Values
Abstract
Discovering knowledge from huge databases with missing values is a challenging problem in Data Mining. In this paper, a novel hybrid algorithm for learning knowledge represented in Bayesian Networks is discussed. The new algorithm combines an evolutionary algorithm with the Expectation-Maximization (EM) algorithm to overcome the problem of getting stuck in sub-optimal solutions which occurs in most existing learning algorithms. The experimental results on the databases generated from several benchmark network structures illustrate that our system outperforms some state-of-the-art algorithms. We also apply our system to a direct marketing problem, and compare the performance of the discovered Bayesian networks with the response models obtained by other algorithms. In the comparison, the Bayesian networks learned by our system outperform others.
Yuan Yuan Guo, Man Leung Wong
Fuzzy Local Currency Based on Social Network Analysis for Promoting Community Businesses
Summary
This paper discusses the ability of local currencies (LCs) to exchange goods and/or services by introducing a method to analyze the reciprocity of communities based on fuzzy network analysis. LCs are expected to revitalize social communities that face difficulties due to the attenuation of human relations. Therefore, such currencies have drastically spread all over the world to resolve these difficulties. LCs circulate in particular areas or communities and enhance social capitals. The significance of reciprocity in a community is usually referred to in light of the non-additivity of evaluation measures that reflect the non-additivity of relationships among community members and/or their activities. To analyze such reciprocity, we employ a fuzzy measure based on fuzzy network analysis that provides certain guidelines for the emergence of interpersonal relationalities among community members.
Osamu Katai, Hiroshi Kawakami, Takayuki Shiose
Evolving Failure Resilience in Scale-Free Networks
Summary
Today our society tends to become more and more dependent on large scale (global) infrastructure networks. In many cases, attacks on a few important nodes of such systems lead to irreparable local or, worse, global damages. Thus, designing resilient networks rather than reducing the effects of some unexpected attacks becomes a must. As the most resilient network, regarding any kind of attacks, should be a full-connected graph, it is obvious that implementing such a network is a utopia. This paper proposes an original multi-objective method for optimizing complex networks’ structure, taking into account the implementation costs. A micro genetic algorithm is used in order to improve networks’ resilience to targeted attacks on HUB nodes while keeping the implementation costs as low as possible.
George Leu, Akira Namatame
Evolving Networks with Enhanced Linear Stability Properties
Abstract
Networks are so much a part of our modern society that when they fail the effects can be significant. In many cases, global network failures can be triggered by seemingly minor local events. Increased understanding of why this occurs and, importantly, the properties of the network that allow it to occur, is thus desirable. In this account we use an evolutionary algorithm to evolve complex networks that have enhanced linear stability properties. We then analyze these networks for topological regularities that explain the source their stability/instability. Analysis of the structure of networks with enhanced stability properties reveals that these networks have a highly skewed degree distribution, very short path-length between nodes, have little or no clustering and are dissasortative. By contrast, networks with enhanced instability properties have a peaked degree distribution with a small variance, have long path-lengths between nodes, contain a high degree of clustering and are highly assortative. We then test the topological stability of these networks and discover that networks with enhanced stability properties are highly robust to the random removal of nodes, but highly fragile to targeted attacks. Networks with enhanced instability properties are robust to targeted attacks. These network features have implications for the physical and biological networks that surround us.
David Newth, Jeff Ash
Effectiveness of Close-Loop Congestion Controls for DDoS Attacks
Summary
High-bandwidth traffic aggregates may occur during times of flooding-based distributed denial-of-service (DDoS) attacks which are also known as flash crowds problems. Congestion control of these traffic aggregates is important to avoid congestion collapse of network services. We perform fundamental researches to minimize the effect using existing congestion controls. We simulate DDoS attacks in different Internet topologies (Tiers model, Transit-Stub model, Scale-free model). We try to improve network resistance against DDoS attacks and similar overflow problems by using open-loop and close-loop congestion controls such as Droptail, RED and CHOKe. Furthermore we propose a new congestion contorl method based on protocol type of flow and compare the performance with existing methods.
Takanori Komatsu, Akira Namatame
Priority-Based Genetic Algorithm for Shortest Path Routing Problem in OSPF
Abstract
With the growth of the Internet, Internet service providers try to meet the increasing traffic demand with new technology and improved utilization of existing resources. Routing of data packets is the most important way to improve network utilization. Open Shortest Path First (OSPF) is the first widely deployed routing protocol that could converge a network in the low seconds, and guarantee loop-free paths. In this paper, we propose a new shortest path routing algorithm by using a priority-based Genetic Algorithm (priGA) approach in OSPF. Different with traditional Dijkstra’s algorithms, GAs provide us great flexibility, robustness and adaptability to make efficient implementations for specific routing problems, such as Quality of Service (QoS) requirements, OSPF weight setting etc. Numerical experiments with various scales of network problems show the effectiveness and the efficiency of our approach by comparing with the recent researches.
Lin Lin, Mitsuo Gen
Evolutionary Network Design by Multiobjective Hybrid Genetic Algorithm
Abstract
Network design is one of the most important and most frequently encountered classes of optimization problems. It is a combinatory field in combinatorial optimization and graph theory. When considering a bicriteria network design (bND) problem with the two conflicting objectives of minimizing cost and maximizing flow, network design problems where even one flow measure be maximized, are often NP-hard problems. But, in real-life applications, it is often the case that the network to be built is required to optimize multi-criteria simultaneously. Thus the calculation of the multi-criteria network design problems is a difficult task. In this paper, we propose a new multiobjective hybrid genetic algorithm (mo-hGA) hybridized with Fuzzy Logic Control (FLC) and Local Search (LS). Numerical experiments show the effectiveness and the efficiency of our approach by comparing with the recent researches.
Mitsuo Gen, Lin Lin, Jung-Bok Jo
Hybrid Genetic Algorithm for Designing Logistics Network, VRP and AGV Problems
Abstract
The use of hybrid genetic algorithm (Hybrid GA) in the networks design has been growing the last decades due to the fact that many practical networks design problems are NP hard. This paper examines recent developments in the field of evolutionary optimization for network design. We combine the various hybrid genetic algorithms to a wide range of practical network problems such as a logistics network model, VRP (Vehicle Routing Problem), and AGV (Automated Guided Vehicles) dispatching problem. It is covered as follows: first, we apply the hybrid priority-based GA for solving fixed-charge Transportation Problem (fcTP), which the proposed approach is more effective in larger size than benchmark test problems. Second, we give the several resent GA approach for solving Multistage Logistic Network Problem. Third, we introduce Vehicle Routing Problem (VRP) and variants of VRP. We apply the priGA for solving Multi-depot vehicle routing problem with time windows (mdVRP-tw). Lastly, we apply a priority-based based GA to solve an automated guided vehicles (AGV) dispatching problem in Flexible Manufacturing System (FMS).
Mitsuo Gen, Lin Lin, Jung-Bok Jo
Multiobjective Genetic Algorithm for Bicriteria Network Design Problems
Abstract
Network design is one of the most important and most frequently encountered classes of optimization problems. However, various network optimization problems typically cannot be solved by a generalized approach. Usually we must design the different algorithm for the different type of network optimization problem depending on the characteristics of the problem. In this paper, we try to investigate with a broad spectrum of multi-criteria network design models, analyze the recent related researches, design and validate new effective multiobjective hybrid genetic algorithms for three kinds of major bicriteria network design models: bicriteria shortest path (bSP) model, bicriteria minimum spanning tree (bST) model and bicriteria network flow (bNF) model. Because of the adaptability, robustness and flexibility of the evolutionary algorithms, proposed approaches are easy applied to many kinds of real applications extended from these major network design models.
Lin Lin, Mitsuo Gen
Use of Serendipity Power for Discoveries and Inventions
Abstract
The word “serendipity,” introduced to scientific fields by R. K. Merton makes discoveries by accidents and sagacity and has long life since the eighteenth century. Its power was experimentally studied for education in scientific observations by R. S. Lenox. In this paper, in a simple model we analyzed the power of serendipity with two important factors: accident and sagacity. A method to improve its power is also presented based on a mechanism of a serendipity phenomenon that works effectively.
Shigekazu Sawaizumi, Osamu Katai, Hiroshi Kawakami, Takayuki Shiose
Evolution of Retinal Blood Vessel Segmentation Methodology Using Wavelet Transforms for Assessment of Diabetic Retinopathy
Introduction
Diabetes is a chronic disease that affects the body’s capacity to regulate the amount of sugar in the blood. One in twenty Australians are affected by diabetes, but this figure is conservative, due to the presence of subclinical diabetes, where the disease is undiagnosed, yet is already damaging the body without manifesting substantial symptoms. This incidence rate is not confined to Australia, but is typical of developed nations, and even higher in developing nations. Excess sugar in the blood results in metabolites that cause vision loss, heart failure and stroke, and damage to peripheral blood vessels.These problems contribute significantly to the morbidity and mortality of the Australian population, so that any improvement in early diagnosis would therefore represent a significant gain. The incidence is projected to rise, and has already become a major epidemic [16].
D. J. Cornforth, H. F. Jelinek, M. J. Cree, J. J. G. Leandro, J. V. B. Soares, R. M. Cesar Jr.
Multistage-Based Genetic Algorithm for Flexible Job-Shop Scheduling Problem
Abstract
Flexible job shop scheduling problem (fJSP) is an extension of the traditional job shop scheduling problem (JSP), which provides a closer approximation to real scheduling problems. In this paper, a multistage-based genetic algorithm with bottleneck shifting is developed for the fJSP problem. The genetic algorithm uses two vectors to represent each solution candidate of the fJSP problem. Phenotype-based crossover and mutation operators are proposed to adapt to the special chromosome structures and the characteristics of the problem. The bottleneck shifting works over two kinds of effective neighborhood, which use interchange of operation sequences and assignment of new machines for operations on the critical path. In order to strengthen the search ability, the neighborhood structure can be adjusted dynamically in the local search procedure. The performance of the proposed method is validated by numerical experiments on three representative problems.
Mitsuo Gen, Jie Gao, Lin Lin
Implementation of Parallel Genetic Algorithms on Graphics Processing Units
Abstract
In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Units (GPUs) which are available and installed on ubiquitous personal computers. HGA extends the classical genetic algorithm by incorporating the Cauchy mutation operator from evolutionary programming. In our parallel HGA, all steps except the random number generation procedure are performed in GPU and thus our parallel HGA can be executed effectively and efficiently. We suggest and develop the novel pseudo-deterministic selection method which is comparable to the traditional global selection approach with significant execution time performance advantages.We perform experiments to compare our parallel HGA with our previous parallel FEP (Fast Evolutionary programming) and demonstrate that the former is much more effective and efficient than the latter. The parallel and sequential implementations of HGA are compared in a number of experiments, it is observed that the former outperforms the latter significantly. The effectiveness and efficiency of the pseudo-deterministic selection method is also studied.
Man Leung Wong, Tien Tsin Wong
Backmatter
Metadata
Title
Intelligent and Evolutionary Systems
Editors
Mitsuo Gen
David Green
Osamu Katai
Bob McKay
Akira Namatame
Ruhul A. Sarker
Byoung-Tak Zhang
Copyright Year
2009
Publisher
Springer Berlin Heidelberg
Electronic ISBN
978-3-540-95978-6
Print ISBN
978-3-540-95977-9
DOI
https://doi.org/10.1007/978-3-540-95978-6

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