Advanced Genetic Operators Research Articles

Design of fuzzy expert system for microarray data classification using a novel Genetic Swarm Algorithm

Knowledge gained through classification of microarray gene expression data is increasingly important as they are useful for phenotype classification of diseases. Different from black box methods, fuzzy expert system can produce interpretable classifier with knowledge expressed in terms of if-then rules and membership function. This paper proposes a novel Genetic Swarm Algorithm (GSA) for obtaining near optimal rule set and membership function tuning. Advanced and problem specific genetic operators are proposed to improve the convergence of GSA and classification accuracy. The performance of the proposed approach is evaluated using six gene expression data sets. From the simulation study it is found that the proposed approach generated a compact fuzzy system with high classification accuracy for all the data sets when compared with other approaches.

A parallel genetic programming based intelligent miner for discovery of censored production rules with fuzzy hierarchy

Automated discovery of rules with exceptions and hierarchical structures is an important problem in data mining. A knowledge structure based on Censored Production Rules with Fuzzy Hierarchy (CPRFH) not only provides an excellent mechanism for handling exceptions but also captures the hierarchical relationship among the classes in the dataset. Moreover, CPRFHs are able to exhibit variable precision logic for approximate reasoning. This paper proposes discovery of knowledge in the form of CPRFHs using island model of genetic programming with two advanced genetic operators, namely; fission and fusion. The fission and fusion operators impart intelligence to the system as these operators discover new classes/concepts which are not present explicitly in the data set being mined. A suitable encoding with syntactic constraints is designed and an appropriate fitness function is suggested to measure the goodness of the hierarchies. The experimental results confirm that the island model with fission and fusion outperforms the sequential as well as the island models without fission and fusion in terms of correctness of the solution arrived and size of the trees evolved.

Improved genetic algorithm for optimal design of fuzzy classifier

One of the important issues in the design of fuzzy classifier is the formation of fuzzy if-then rules and the membership functions. This paper presents a Genetic Algorithm (GA) approach to obtain the optimal rule-set and the membership function. To develop the fuzzy system the membership functions and rule-set are encoded into the chromosome and evolved simultaneously using GA. Advanced genetic operators are applied to improve the performance of the GA in designing the fuzzy classifier. The performance of the proposed approach is demonstrated through development of fuzzy classifier for Iris, Wine and tcpdump data. From the simulation study, it is found that the improved GA produces a fuzzy classifier which has minimum number of rules and high classification accuracy. Statistical analysis of the test results shows the superiority of the proposed algorithm over the existing methods.

Optimization and Improvement in Robot-Based Assembly Line System by Hybrid Genetic Algorithm

In the real world, there are a lot of scenes from which the product is made by using the robot, which needs different assembly times to perform a given task, because of its capabilities and specialization. For a robotic assembly line balancing (rALB) problem, a set of tasks have to be assigned to stations, and each station needs to select one robot to process the assigned tasks. In this paper, we propose a hybrid genetic algorithm (hGA) for solving this problem. In the hGA, we use new representation method. Advanced genetic operators adapted to the specific chromosome structure and the characteristics of the rALB problem are used. In order to strengthen the search ability, a local search procedure is integrated under the framework the genetic algorithm. Some practical test instances demonstrate the effectiveness and efficiency of the proposed algorithm.

Adaptive Enhanced Genetic Algorithm‐Based Proportional Integral Controller Tuning for pH Process

The control of a pH process is a challenging task. For the pH process, Proportional Integral (PI) control has been successfully used for many years. Tuning of the PI controller is necessary for the satisfactory operation of the system. This paper presents an Enhanced Genetic Algorithm (EGA) for the tuning of PI controllers. To avoid the premature convergence and to reduce the computation time, advanced genetic operators have been used in the proposed GA approach. The proposed GA tuned PI controller is implemented in the experimental pH process. The performance of the controller with Enhanced GA tuning is compared with a traditional Ziegler Nichols tuning and Internal Model control for various set point and trajectory responses of the pH process. The experimental results show that the proposed GA is very much capable to adapt the controller to dynamic plant characteristics changes in pH process.

Parameter identification of vehicles moving on continuous bridges

This paper describes a method for the identification of parameters of vehicles moving on multi-span continuous bridges. Each moving vehicle is modelled as a 2-degree-of-freedom system that comprises four components: an unsprung mass and a sprung mass, which are connected together by a damper and a spring. The corresponding parameters of these four components, namely, the equivalent unsprung mass and sprung mass, the damping coefficient and the spring stiffness are identified based on dynamic simulation of the vehicle–bridge system. The identification process makes use of acceleration measurements at selected stations on the bridge. In the study, the acceleration measurements are simulated from the solution to the forward problem of a continuous beam under moving vehicles, together with the addition of artificially generated measurement noise. The identification is carried out through a robust multi-stage optimization scheme based on genetic algorithms, which searches for the best estimates of parameters by minimizing the errors between the measured accelerations and the reconstructed accelerations from the moving vehicles. This multi-stage optimization scheme reduces the variable search domains stage by stage using the identified results of the previous stage. Besides the basic operators in simple genetic algorithms, some advanced genetic operators and techniques are adopted here. Therefore, it makes the proposed identification procedure much more efficient than other traditional optimization methods. The identification procedure is then verified with a few test cases. The estimated vehicle parameters can also be used to get the time varying contact forces between the vehicles and bridge surface.

Synthesis of waste interception and allocation networks using genetic-alopex algorithm

In this paper, the hybrid genetic-alopex algorithm (GAA) is proposed to solve the problem of synthesis of waste interception and allocation networks (WINs). Algorithm of pattern extraction (alopex) adopts ‘noises’ generated randomly to cast off local optima and enhances the capacity of hillclimbing. Genetic algorithms (GA) is good at keeping evolving of total population and parallel computing. The combination of GA and alopex enhances the algorithm in that it converges at the global optimum easily and accelerates the convergence to some extent. In addition, several advanced genetic operators are adopted to protect diversity of the population. Results of testing functions and a case study show that GAA is stable and can converge to global optimum well for the problem of high dimensions.

A STRUCTURAL OPTIMIZATION APPROACH AND SOFTWARE BASED ON GENETIC ALGORITHMS AND FINITE ELEMENTS

The use of new evolutionary techniques such as genetic algorithms in optimization has increased lately due to their efficiency in solving non-conventional optimization problems. This fact has led to this work in which genetic algorithms and structural finite-clement analysis methods are combined to develop a useful approach to optimize the geometry, size and shape of a structure. A brief overview of genetic algorithms is presented and discussed as well as some classical and advanced genetic operators, which have demonstrated to be useful in structural optimization. Some guidelines showing how the developed software works are presented as well as flowcharts. Finally, two illustrative engineering examples are discussed in order to show the versatility of both the approach and the computational tool.

Genetic algorithms for optimization in predictive control

Genetic algorithms (GAs) are optimization methods inspired by natural biological evolution. GAs have been successfully applied to a variety of complex optimization problems where other techniques have often failed. The aim of this paper is to investigate the use of GAs for optimization in nonlinear model-based predictive control. Advanced genetic operators and other new features are introduced to increase the efficiency of the genetic search. In order to deal with real-time constraints, termination conditions are proposed to abort the evolution, once a defined level of optimality is reached. Simulated pressure dynamics of a batch fermenter are considered as an example of a highly nonlinear system. Simulation results with GAs are compared with the branch-and-bound method, in terms of the control accuracy and computational costs achieved.

Genetic algorithm optimization applied to electromagnetics: a review

Genetic algorithms are on the rise in electromagnetics as design tools and problem solvers because of their versatility and ability to optimize in complex multimodal search spaces. This paper describes the basic genetic algorithm and recounts its history in the electromagnetics literature. Also, the application of advanced genetic operators to the field of electromagnetics is described, and design results are presented for a number of different applications.

Advanced engineered-conditioning genetic approach to power economic dispatch

Computational efficiency and reliability are the major concerns in the application of genetic algorithms (GAS) to practical problems. Effort has been made in two directions to improve the performance of GAs: the investigation of advanced genetic operators and the development of genetic algorithm hybrids. In this paper, an advanced engineered-conditioning genetic algorithm hybrid (AEC-GA) is proposed, which is a combination strategy involving local search algorithms and genetic algorithms. Moreover, several advanced techniques which enhance program efficiency and accuracy, such as elite policy, adaptive mutation prediction, nonlinear fitness mapping and different crossover techniques, are explored. Using power economic dispatch problems as a basis for comparisons, the outcome of the study clearly demonstrates the advantages of the AEC-GA.