A Genetic Algorithm-Based Approach for Classification Rule Discovery

A heuristic approach to optimization of complex physicochemical processes in the form of a genetic algorithm for solving problems is presented. In comparison with other evolutionary methods, the genetic algorithm allows working with large search spaces and complex evaluation functions, which is especially important in the study of multifactor physicochemical systems. Due to the relatively high need for computing resources when working with large and complex search spaces, optimization of existing calculation organization schemes has a positive effect on the accuracy of the calculated results. The paper presents a modified genetic algorithm that minimizes the number of iterations to achieve a given accuracy when solving the problem of finding the optimal composition of the initial reaction mixture. For a complex physicochemical process, an optimization problem is formulated which consists in finding the composition of the initial reaction mixture that promotes maximization (or minimization) of a given target parameter. The optimality criterion is determined by the type of the problem being solved and, when organizing calculations, is focused on the maximum yield of the target product. The main steps of implementing the genetic algorithm include creating an initial set of solutions and subsequent iterative evaluation of their quality for subsequent combination and modification until optimal values are achieved using mechanisms similar to biological evolution. To improve the efficiency of the method and reduce the number of iterations, a modification of the genetic algorithm is proposed which boils down to a dynamic estimate of the “mutation” parameter, depending on the diversity of individuals in the formed population of solutions. In a series of computational experiments, an analysis was made of the influence of the genetic algorithm parameters on the accuracy and efficiency of solving the problem using the example of studying the kinetics of the Michaelis-Menten enzymatic reaction. The results of calculations to determine the optimal composition of the reaction mixture showed that the dynamic determination of the “mutation” parameter contributes to an increase in the accuracy of the problem solution and a multiple decrease in the relative error value reaching 0.77 % when performing 200 iterations and 0.21 % when performing 400 iterations. The presented modified approach to solving the optimization problem is not limited by the type and content of the studied physicochemical process. The calculations performed showed a high degree of influence of the “mutation” parameter on the accuracy and efficiency of the problem solution, and dynamic control of the value of this parameter allowed increasing the speed of the genetic algorithm and reduce the number of iterations to achieve an optimal solution of a given accuracy. This is especially relevant in the study of multifactorial systems when the influence of parameters is non-trivial.

Temperature control is the key of Ruhrstahl-Heraeus (RH) process in steelmaking plant. The accuracy of RH control model greatly affects the molten steel temperature fluctuation. To obtain RH control model with higher accuracy, an improved case-based reasoning (CBR) model based on attribute weights optimized by genetic algorithm (GA) was proposed. The fitness function in GA was determined according to the prediction accuracy of end temperature of molten steel in RH; then, GA is used to optimize all the attribute weights based on known case base. An improved CBR model that contains the optimized attribute weights was applied to predict end temperature of molten steel in RH, and the prediction accuracy was calculated. Four methods, CBR based on attribute weights optimized by GA (CBR–GA), ordinary CBR, back propagation neural network (BPNN) and multiple linear regression (MLR) method were employed for comparison. The results show that in the error range of [− 3 °C, 3 °C], [− 5 °C, 5 °C], [− 7 °C, 7 °C] and [− 10 °C, 10 °C], the prediction accuracy of CBR–GA was improved by 19.99%, 28.19%, 27.11% and 16.3%, respectively, than that of MLR. Compared with BPNN, the prediction accuracy increased by 3.22%, 7.44%, 5.29% and 2.40%, respectively. Compared with ordinary CBR, the accuracy increased by 5.43%, 5.80%, 4.66% and 2.27%, respectively.

Crop disease leaf image segmentation is a key step in crop disease recognition. In the paper, a segmentation method of crop disease leaf image is proposed to segment leaf image with non-uniform illumination based on maximum entropy and genetic algorithm (GA). The information entropy is regarded as the fitness function of GA, the maximum entropy as convergence criterion of GA. After genetic operation, the optimal threshold is obtained to segment the image of disease leaf. The experimental results of the maize disease leaf image show that the proposed method can select the threshold automatically and efficiently, and has an advantage over the other three algorithms, and also can reserve the main spot features of the original disease leaf image.

As computer and network technology advance, multimedia data can be transferred in real time on the Internet. The increasing user demands for various multimedia data make VOD (Video-on-Demand) services to be developed. VOD services are being used in lots of fields, such as entertainment, distance learning, home shopping, and interactive news. In comparison to the existing HTTP services, these VOD services have special features that the time for their services is longer and the services request more disks and network bandwidths. Therefore, compared to HTTP service environments, VOD services have some different workload patterns. In these VOD service environments, the existing load balancing algorithms researched before are not proper. In this paper, we propose a new load balancing algorithm that is based on the history of past user access patterns to make server loads even on hierarchically distributed VOD system environments. This algorithm uses a dynamic genetic algorithm. The proposed distributed VOD system environment consists of a number of VOD servers, which are distributed geographically, and control servers that manage each group of VOD servers. User requests are distributed to prevent convergence by distributing VOD servers geographically. We use a genetic algorithm based on history data to distribute user requests in a local service area. The information of user requests and services is stored and referred in a database as history data. By applying these history data to the fitness function of genetic algorithms, we implemented the genetic algorithm and operations for VOD systems. The load balancing algorithm proposed in this paper can distributed workloads by predicting workloads precisely on VOD environments.

In this paper, a dynamic path planning scheme based on genetic algorithm (GA) is presented for navigation and obstacle avoidance of mobile robot under unknown environment. The real coding, fitness function and specific genetic operators are devised in the algorithm. The unique coding technique decreases the conventional computational complexity of genetic algorithm. It also speeds up the execution of searching by projecting two dimensional data to one dimensional data, which reduce the size of search space. The fitness function of genetic algorithm takes full consideration of three factors: the collision avoidance path, the shortest distance and smoothness of the path. The specific genetic operators are also selected to make the genetic algorithm more effective. The simulation experiments are made under the VC++ 6.0 environment. The simulation results verify that the genetic algorithm is high effective under various complex dynamic environments.

Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

Shipyards have large departments or facilities. It is essential to make an effective topological layout plan since the initial investment cost of these departments is high. Topological layout is an optimization problem and Genetic Algorithm (GA) is generally used in the literature. The selection of effective genetic algorithm approaches and operators are very important to improve the performance of the optimization. This study investigates an effective solution to the shipyard topological layout using a Quadratic Assignment Problem (QAP) model with classic and elitist GA approaches. Besides, genetic operators that have significant effects on exploitation and exploration capabilities are analyzed. Therefore, 126 experiments were run with 13 different operators. The results obtained from the classic and elitist GA approach were evaluated individually and compared with each other. It was observed that the elitist GA approach has a superior performance compared to the classic GA approach. This study is the most comprehensive and practical study on the performance of the GA for topological layout of the shipyard in the literature.

The GA(genetic algorithms) was applied to find routing trees with minimal maximal sensor load thus extend network lifetime in wireless sensor networks. However, the existing algorithmlimits search space of GA to avoid generating cycles by dividing sensors into layers and stipulating that a sensor only can select a sensor in its lower layer as its new parent. In this paper, we have found that how cycles are generated during GA operations, and proposed a solution based on subtree to avoid generating cycles during GA operations.The solution only requires a sensor not to change its parent to a sensor in the subtree with it as the root. And layers are no longer considered. As a result, GA has more search space and GA operations can be performed more freely. Thus more optimal routing trees are obtained and network lifetime is extended. The experiment shows that our proposed algorithm extend network lifetime notably.

We review the recent work done by our group on theoretically analyzing the behavior of genetic algorithms(GAs) and searching for new ideas and strategies to design more efficient GAs. By introducing the concept of degree of population diversity and using the tools from finite Markov chain, we presented fairly clear characterizations of premature convergence in GAs and proved some results on the relationships between premature convergence and GA’s control parameters such as the population size, the mutation and crossover probabilities, and some population statistics. In the mean time, we have also obtained a result that depicts the search ability of the crossover operators in GAs. An upper bound on the convergence rate for the GAs has been established. Based on the theoretical analyses, we have proposed a novel GA called Genetic Algorithm with No Genetic Operators (GANGO), which needs neither to maintain a population nor to use the conventional genetic operators, and yet has the same search mechanisms as the classical GAs. As another development in improving the performance of GAs, a new concept called Dual Fitness Function was proposed which, when incorporated into the classical GAs will enable the algorithms to judge whether the current population is in the local or global attractive basins. And in this way, the control parameters of GAs can be adjusted adaptively. Some applications of our results to fields such as fuzzy systems, multi-destination routing problem (MDR) are briefly

Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practical adoption. Among various reasons for this, one is the lack of approaches that allow for informative path planning in high-dimensional spaces and non-trivial sensor constraints. In this work we present a sampling-based approach that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a global path that maximizes information gain for the given path budget constraints. We discuss the details of our implementation for an example use case of searching for multiple objects of interest in a large search space using a fixed-wing UAV with a forward-facing camera. We compare our approach to a sampling-based planner baseline and demonstrate how our contributions allow our approach to consistently out-perform the baseline by 18.0%. With this we thus present a practical and generalizable informative path planning framework that can be used for very large environments, limited budgets, and high dimensional search spaces, such as robots with motion constraints or high-dimensional configuration spaces. [Code] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup> Codebase: https://github.com/castacks/tigris [Video] <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sup> Video: https://youtu.be/bMw5nUGL5GQ

The main objective of image browsers is to empower users to find a desired image with ease, speed and accuracy from a large database. In this paper we present a novel approach at creating an image browsing environment based on human perception with the aim of providing intuitive image navigation. In our approach, similarity judgments form the basic structural organization for the images in our browser. To enrich this we have developed a scalable crowd sourced method of augmenting a database with a large number of additional samples by capturing human judgments from members of a crowd. Experiments were conducted involving two databases that demonstrate the effectiveness of our method as an intuitive, fast browsing environment for large image databases.

Power transformer dissolved gases concentrations have characteristics of single-peak. So, improved Gray Verhulst model was proposed in this paper and introduced into Power transformer dissolved gases concentrations prediction. Improved Gray Verhulst model used background function parameter ¿ instead of 0.5 in the background function of original Gray Verhulst model. There are two selection rules of background function parameter ¿, one is prediction error method, the another is posteriori error test method. Because prediction results of both rules have their own shortcoming, a new selection rules which uses Gray relational grade was proposed, and introduced to improved Gray Verhulst model in this paper. Genetic algorithm was used to select background function parameter ¿, and Genetic algorithm parameters were selected by experiments. Gray relational grade was the fitness function in Genetic algorithm. Experiments and comparison with improved Gray Verhulst model demonstrate that the algorithm proposed in this paper has higher prediction accuracy than Gray model, and is feasible and dependable.

Control of a polymerization reactor by fuzzy control method with genetic algorithm

A method is proposed, and shown to be able to find active compounds in a Large Database by iterative selection/screening cycles. The method is derived from genetic algorithms. Starting from an initial small parent population the next compounds to screen are selected by similarity search based on the structural features of two high rating parents. The performance of the method is demonstrated using 20000 compounds with biological data from the NCI1 database.

Very large databases contain large amounts of interrelated information. This information is often stored in relational databases with hundreds of tables and thousands of rows per table. Clustering is an effective way to reduce the information-overhead associated with finding information among these tables, allowing the user to browse through the clusters as well as the individual tables. In this paper, we compare the use of two adaptive algorithms (genetic algorithms, and neural networks) in clustering the tables of a very large database. These clusters allow the user to index into this overwhelming number of tables and find the needed information quickly. We cluster the tables based on the user’s queries and not on the content of the tables, thus the clustering reflects the unique relationships each user sees among the tables. The original database remains untouched, however each user will now have a personalized index into this database.