Genetic network programming based rule accumulation for agent control

制度:新 ; 報告番号:甲3768号 ; 学位の種類:博士(工学) ; 授与年月日:2013/1/28 ; 早大学位記番号:新6141Waseda Universit

Data mining and classification for traffic systems using genetic network programming

制度:新 ; 報告番号:甲3271号 ; 学位の種類:博士(工学) ; 授与年月日:2011/3/15 ; 早大学位記番号:新557

Analysis of the Dynamical Behavior of Firms in a Three Layered Modular Assembly Model

This paper formulate an option model considers supplier's reaction as the profit sharing in module production, and analyses it by the agent theory. A dynamic environment in the model of the system of the production of modules of three layers is assumed, and the maker and the supplier are modeled by the technique of Genetic Programming (GP) as an agent who takes the action of selfoptimization. As result, the condition that the agent can exist continuously in the market is requested. In conclusion, violent competition and the selection of the similar agent are found even in the model of the option to consider the profit sharing and the reaction

Evolving investment models using genetic network programming and genetic relation algorithm

制度:新 ; 報告番号:甲3442号 ; 学位の種類:博士(工学) ; 授与年月日:15-Sep-11 ; 早大学位記番号:新576

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

An Investigation Into a Hybrid Genetic Programming and Ant Colony Optimization Method for Credit Scoring

This thesis proposes and investigates a new hybrid technique based on Genetic Programming (GP) and Ant Colony Optimization (ACO) techniques for inducing data classification rules. The proposed hybrid approach aims to improve on the accuracy of data classification rules produced by the original GP technique, which uses randomly generated initial populations. This hybrid technique relies on the ACO technique to produce the initial populations for the GP technique. To evaluate and compare their effectiveness in producing good data classification rules, GP, ACO, and hybrid techniques were implemented in the C programming language. The data classification rules were created and evaluated by executing these codes with two datasets for credit scoring problems, widely known as the Australian and German datasets, available from the Machine Learning Repository at the University of California, Irvine. The experimental results demonstrate that although all tree techniques yield similar accuracy during testing, on average, the hybrid ACO-GP approach performs better than either GP or ACO during training

Ant colony optimization approach for stacking configurations

In data mining, classifiers are generated to predict the class labels of the instances. An ensemble is a decision making system which applies certain strategies to combine the predictions of different classifiers and generate a collective decision. Previous research has empirically and theoretically demonstrated that an ensemble classifier can be more accurate and stable than its component classifiers in most cases. Stacking is a well-known ensemble which adopts a two-level structure: the base-level classifiers to generate predictions and the meta-level classifier to make collective decisions. A consequential problem is: what learning algorithms should be used to generate the base-level and meta-level classifier in the Stacking configuration? It is not easy to find a suitable configuration for a specific dataset. In some early works, the selection of a meta classifier and its training data are the major concern. Recently, researchers have tried to apply metaheuristic methods to optimize the configuration of the base classifiers and the meta classifier. Ant Colony Optimization (ACO), which is inspired by the foraging behaviors of real ant colonies, is one of the most popular approaches among the metaheuristics. In this work, we propose a novel ACO-Stacking approach that uses ACO to tackle the Stacking configuration problem. This work is the first to apply ACO to the Stacking configuration problem. Different implementations of the ACO-Stacking approach are developed. The first version identifies the appropriate learning algorithms in generating the base-level classifiers while using a specific algorithm to create the meta-level classifier. The second version simultaneously finds the suitable learning algorithms to create the base-level classifiers and the meta-level classifier. Moreover, we study how different kinds on local information of classifiers will affect the classification results. Several pieces of local information collected from the initial phase of ACO-Stacking are considered, such as the precision, f-measure of each classifier and correlative differences of paired classifiers. A series of experiments are performed to compare the ACO-Stacking approach with other ensembles on a number of datasets of different domains and sizes. The experiments show that the new approach can achieve promising results and gain advantages over other ensembles. The correlative differences of the classifiers could be the best local information in this approach. Under the agile ACO-Stacking framework, an application to deal with a direct marketing problem is explored. A real world database from a US-based catalog company, containing more than 100,000 customer marketing records, is used in the experiments. The results indicate that our approach can gain more cumulative response lifts and cumulative profit lifts in the top deciles. In conclusion, it is competitive with some well-known conventional and ensemble data mining methods

Understanding over-indebtedness in Portugal: descriptive and predictive models.

Over-indebtedness is a recurring problem in Portugal. After facing different economic cycles, between financial crises and prosperity periods, Portuguese consumers have been striving to keep their household finances stable and avoid being over-indebted. This project aims to gain insights on over-indebtedness, from different perspectives that range from the social to the economic point of view. It examines over-indebtedness from a psychological and from a data science perspective. In particular, we suggest that the systemic impact of financial crisis in Portugal not only promotes over-indebtedness, but it crafts a specific profile of over-indebted consumers which may be distinguished from other profiles, ranging from the emphasis on lack of self-regulation and careless management of one’s budget to other causal factors such as consumerism, crisis, and unemployment. Given this scenario, this project proposes the use of Machine Learning (ML) for developing descriptive and predictive models, to understand the influencing factors of over-indebtedness on Portuguese consumers and will be used for establishing consumer clusters and guidelines for over-indebtedness regulation and consumer financial empowerment.info:eu-repo/semantics/publishedVersio

Fuzzy Logic Based DSR Trust Estimation Routing Protocol for MANET Using Evolutionary Algorithms

In MANET attaining consistent routing is a main problem due to several reasons such as lack of static infrastructure, exposed transmission medium, energetic network topology and restricted battery power. These features also create the scheme of direction-finding protocols in MANETs become even more interesting. In this work, a Trust centered routing protocol is suggested, since trust plays a vital role in computing path in mobile ad hoc networks (MANETs). Estimating and computing trust encourages cooperation in mobile ad hoc networks (MANETs). Various present grade systems suddenly estimate the trust by considering any one of the parameters such as energy of node, number of hops and mobility. Estimating trust is an Energetic multi objective optimization problem (EMOPs) typically including many contradictory goals such as lifetime of node, lifetime of link and buffer occupancy proportion which change over time. To solve this multi objective problem, a hybrid Harmony Search Combined with Genetic algorithm and Cuckoo search is used along with reactive method Dynamic Source routing protocol to provide the mobile hosts to find out and sustain routes between the origin node (SN) to the target node (TN). In this work, the performance of the direction-finding practice is assessed using throughput, end to end delay, and load on the network and route detection period