Wei-Hsiu Huang

Evolving and clustering fuzzy decision tree for financial time series data forecasting

Stock price predictions have always been a subject of interest for investors and professional analysts. Nevertheless, determining the best time to buy or sell a stock remains very difficult because there are many factors that may influence the stock prices. This paper establishes a novel financial time series-forecasting model by evolving and clustering fuzzy decision tree for stocks in Taiwan Stock Exchange Corporation (TSEC). This forecasting model integrates a data clustering technique, a fuzzy decision tree (FDT), and genetic algorithms (GA) to construct a decision-making system based on historical data and technical indexes. The set of historical data is divided into k sub-clusters by adopting K-means algorithm. GA is then applied to evolve the number of fuzzy terms for each input index in FDT so the forecasting accuracy of the model can be further improved. A different forecasting model will be generated for each sub-cluster. In other words, the number of fuzzy terms in each sub-cluster will be different. Hit rate is applied as a performance measure and the proposed GAFDT model has the best performance of 82% average hit rate when compared with other approaches on various stocks in TSEC.

Dynamic diversity control in genetic algorithm for mining unsearched solution space in TSP problems

The applications of genetic algorithms (GAs) in solving combinatorial problems are frequently faced with a problem of early convergence and the evolutionary processes are often trapped in a local optimum. This premature convergence occurs when the population of a genetic algorithm reaches a suboptimal state that the genetic operators can no longer produce offspring with a better performance than their parents. In the literature, plenty of work has been investigated to introduce new methods and operators in order to overcome this essential problem of genetic algorithms. As these methods and the belonging operators are rather problem specific in general. In this research, we take a different approach by constantly observing the progress of the evolutionary process and when the diversity of the population dropping below a threshold level then artificial chromosomes with high diversity will be introduced to increase the average diversity level thus to ensure the process can jump out the local optimum and to revolve again. A dynamic threshold control mechanism is built up during the evolutionary process to further improve the system performance. The proposed method is implemented independently of the problem characteristics and can be applied to improve the global convergence behavior of genetic algorithms. The experimental results using TSP instances show that the proposed approach is very effective in preventing the premature convergence when compared with other approaches.

A hybrid genetic-immune algorithm with improved lifespan and elite antigen for flow-shop scheduling problems

In this paper, a hybrid genetic-immune algorithm (HGIA) is proposed to reduce the premature convergence problem in a genetic algorithm (GA) in solving permutation flow-shop scheduling problems. A co-evolutionary strategy is proposed for efficient combination of GA and an artificial immune system (AIS). First, the GA is adopted to generate antigens with better fitness, and then the population in the last generation is transformed into antibodies in AIS. A new formula for calculating the lifespan of each antibody is employed during the evolution processes. In addition, a new mechanism including T-cell and B-cell generation procedures is applied to produce different types of antibodies which will be merged together. The antibodies with longer lifespan will survive and enter the next generation. This co-evolutionary strategy is very effective since chromosomes and antibodies will be transformed and evolved dynamically. The intensive experimental results show the effectiveness of the HGIA approach. The hybrid algorithm can be further extended to solve different combinatorial problems.

Artificial chromosomes embedded in genetic algorithm for a chip resistor scheduling problem in minimizing the makespan

The manufacturing processes of a chip resistor are very similar to a flowshop scheduling problem only with minor details which can be modeled using some extra constraints; while permutation flowshop scheduling problems (PFSPs) have attracted much attention in the research works. Many approaches like genetic algorithms were dedicated to solve PFSPs effectively and efficiently. In this paper, a novel approach is presented by embedding artificial chromosomes into the genetic algorithm to further improve the solution quality and to accelerate the convergence rate. The artificial chromosome generation mechanism first analyzes the job and position association existed in previous chromosomes and records the information in an association matrix. An association matrix is generated according to the job and position distribution from top 50% chromosomes. Artificial chromosomes are determined by performing a roulette wheel selection according to the marginal probability distribution of each position. Two types of PFSPs are considered for evaluation. One is a three-machine flowshop in the printing operation of a real-world chip resistor factory and the other is the standard benchmark problems retrieved from OR-Library. The result indicates that the proposed method is able to improve the solution quality significantly and accelerate the convergence process.

Developing a varietal GA with ESMA strategy for solving the pick and place problem in printed circuit board assembly line

The main issue for enhancing the productivity in Printed Circuit Board (PCB) is to reduce the cycle time for pick and place (PAP) operations; i.e., to minimize the time for the PAP operations. According to the characteristics of the PAP problems, the sequence for the placement of components can be mostly treated as the Travelling Salesman Problem (TSP). In this paper, a Genetic Algorithm (GA) with External Self-evolving Multiple Archives (ESMA) is developed for minimizing the PAP operations in PCB assembly line. ESMA focuses on the issue of improving the premature convergence time in GA by adopting efficient measures for population diversity, effective diversity control and mutation strategies to enhance the global searching ability. Three mechanisms for varietal GA such as Clustering Strategy, Switchable Mutation and Elitist Propagation have been designed based on the concept of increasing the dynamic diversity of the population. The experimental results in PCB and TSP instances show that the proposed approach is very promising and it contains the ability of local and global searching. The experimental results show ESMA can further improve the performance of GA by searching the solution space with more promising results.

A Hybrid Genetic-Immune Algorithm with Improved Offsprings and Elitist Antigen for Flow-Shop Scheduling Problems

In this paper, a Hybrid Genetic-Immune algorithm (HGIA) is developed to solve the flow-shop scheduling problems. The regular genetic algorithm is applied in the first-stage to rapidly evolve and when the processes are converged up to a pre-defined iteration then the Artificial Immune System (AIS) is introduced to hybridize Genetic Algorithm (GA) in the second stage. Therefore, HGIA continues to search optimal solution via co-evolutional process. In the co-evolutionary process, GA and AIS cooperate with each other to search optimal solution by searching different objective functions. One is named fitness in GA section and another one is named antigen which will evoke the withstanding of antibodies. In the process of fighting, the antibodies will evolve till they can resist the antigen. An improved survival strategy of lifespan is also proposed to extend the lifespan of the antibodies as a result the selected antibodies will stay in system longer. The hybrid of GA and AIS can simultaneously contain two objectives. Hence, larger searching space and escaping from local optimal solution will be the superiority for hybridizing GA and AIS. In the research, a set of flow-shop scheduling problems are applied for validating the efficiency. The intensive experimental results show the effectiveness of the proposed approach for Flow-shop problems in Production Scheduling.

A two-stage AIS approach for grid scheduling problems

Grid workflow scheduling problem has been a research focus in grid computing in recent years. Various deterministic or meta-heuristic scheduling approaches have been proposed to solve this NP-complete problem. A perusal of published papers on the artificial immune system (AIS) reveals that most researchers use the clonal selection of B cells during the evolving processes and the affinity function of B cells to solve various optimisation problems. This research takes a different approach to the subject – firstly by applying a modified algorithm (Hu, T.C., 1961. Parallel sequencing and assembly line problem. Operations Research, 9 (6), 841–848) to sequence the job and this sequence is applied for further application. Secondly, the derived sequence is then used for machine allocations using the AIS approach. The proposed AIS apply B cells to reduce the antigens and then combining T helper cells and T suppressor cells to solve the grid scheduling problems. Our proposed methodology differs from other earlier approaches as follows: 1. A two-stage approach is applied using a fixed sequence derived from heuristic to allocate machine. 2. AIS apply B cells as bases and then T cells are employed next. T helper cells are used to help improve the solution and then T suppressor cells are generated to increase the diversity of the population. A new formula is proposed to calculate the affinity of the antibody with the antigen. The total difference of completion time of each job is applied instead of the difference of makespan of the schedule. This new AIS method can supplement the flaw of genetic algorithms (GA) using fitness as the basis and a new lifespan which will keep good diversified chromosomes within the population to extend the searching spaces. The experimental tests show that this novel AIS method is very effective when compared with other meta-heuristics such as GA, simulated annealing (SA), and ant colony optimisation (ACO).

A two-phase genetic-immune algorithm with improved survival strategy of lifespan for flow-shop scheduling problems

With the increase in manufacturing complexity, conventional production scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. Therefore, applying efficient algorithm to solve the scheduling problems is essential for reducing the time budget. Genetic Algorithms (GAs) is very effective in solving discrete combinatorial problems but they are frequently faced with a problem of early convergence. During the evolutionary processes, GAs are often trapped in a local optimum. In the literature, plenty of work has been investigated to introduce new methods for overcoming this essential problem of genetic algorithms. In this paper, a two-phase Genetic-Immune algorithm is developed to solve the flow-shop scheduling problems. The regular genetic algorithm is applied in the first-phase and when the processes are converged up to a pre-defined iteration then the Artificial Immune System (AIS) is introduced in the second phase. After the two-phase evolution process, the Genetic Immune Algorithm (GIA) is applied to deal with different objective functions named antigen which will evoke the withstanding of antibodies. In the process of fighting, the antibodies will evolve till they can resist the antigen. An improved survival strategy of lifespan is proposed to extend the lifespan of the antibody so that can keep selected antibodies stay in system longer. Finally, the Two-phase Genetic-Immune Algorithm (TPGIA) is tested on a set of flow-shop scheduling problems. The intensive experimental results show the effectiveness of the proposed approach when compared with other methods.

Dynamic Diversity Control in Genetic Algorithm for Extended Exploration of Solution Space in Multi-Objective TSP

Premature convergence in the process of genetic algorithm (GA) for searching solution is frequently faced and the evolutionary processes are often trapped in a local but not global optimum. This phenomenon occurs when the population of a genetic algorithm reaches a suboptimal state that the genetic operators can no longer produce offspring with a better performance than their parents. In the literature, plenty of work has been investigated to introduce new methods and operators in order to overcome this essential problem of genetic algorithms. As these methods and the belonging operators are rather problem specific in general. In this research, we observe the progress of the evolutionary process, and when the diversity of the population dropping below a threshold level then artificial chromosomes with high diversity will be introduced to increase the average diversity level thus to ensure the process can jump out the local optimum. The proposed approach is implemented independently of the problem characteristics and can be applied to improve the global convergence behavior of genetic algorithms. We eventually apply this approach to solve Multi-Objective (MO) Traveling Salesman Problem (TSP) which were combined KroA with KroB, KroC, KroD and KroE to be trade-off problems. The result shows the solution quality to validate the adaptability of DDCGA for solving such problems.

Memes co-evolution strategies for fast convergence in solving single machine scheduling problems

In recent years, researchers have become more aware of the significance and importance of memes in computational problem‐solving. It is now generally accepted that collectively, memes as a group or population undergo evolution just like genes, competition and collaboration. In this paper, we present a memes co‐evolutionary framework for solving the single machine total weighted tardiness problem. The mechanisms of memes co‐evolution serve to promote diversity not just in the solutions, but also within the memes that participate in the search. Our results show convincingly that the memes co‐evolution strategies are able to improve the performance in solving several difficult benchmarks of weighted tardiness single‐machine scheduling problems.