Proposed Hybrid Genetic Algorithm Research Articles

Sequencing by hybridization: an enhanced crossover operator for a hybrid genetic algorithm

This paper presents a genetic algorithm for an important computational biology problem. The problem appears in the computational part of a new proposal for DNA sequencing denominated sequencing by hybridization. The general usage of this method for real sequencing purposes depends mainly on the development of good algorithmic procedures for solving its computational phase. The proposed genetic algorithm is a modified version of a previously proposed hybrid genetic algorithm for the same problem. It is compared with two well suited meta-heuristic approaches reported in the literature: the hybrid genetic algorithm, which is the origin of our proposed variant, and a tabu-scatter search algorithm. Experimental results carried out on real DNA data show the advantages of using the proposed algorithm. Furthermore, statistical tests confirm the superiority of the proposed variant over the state-of-the-art heuristics.

Structural Optimization of Flat-Face CRT by Using Hybrid Genetic Algorithms

This paper presents the development of a new structural optimization technique and its application to flat-face cathode ray tubes (CRTs). A hybrid genetic algorithm (GA) using four optimization parameters is proposed for application to the design of lightweight and flat-face CRTs. In this unique design procedure, a penalty function method is introduced in order to improve the efficiency of optimization. The optimization was carried out by taking into consideration various limiting conditions associated with the strength of materials, product functions and their manufacturing. It was found that the proposed hybrid GA effectively improves the efficiency and stability of optimum design. Moreover, a prototype of the flat-face CRT was manufactured on this basis. It was proved that the measured stress of the prototype corresponds with the analysis and the weight reduction of the prototype was 11% of a conventional CRT.

Hybrid Genetic Algorithm for Design of Robust Communication Systems

A novel hybrid genetic algorithm (GA) for jointly optimizing source and channel codes is presented in this paper. The algorithm first uses GA for the coarse search of source and channel codes. An iterative search is then followed for the refinement of the coarse search. The hybrid GA enhances the robustness of the design of source and channel codes. The distributed GA scheme can also be used in conjunction with the proposed hybrid GA algorithm for further performance improvement.

Hybrid Genetic Algorithms for Minimizing the Maximum Completion Time for the Wafer Probing Scheduling Problem

The wafer probing scheduling problem (WPSP) is a variation of the parallel-machine problem, which carries with the objective to minimize the total workload to enhance the utilization rate of machine capacity. However, to minimize the maximum completion time for the wafer probing scheduling problem (WPSP) is very important for equally utilizing the capacity of parallel-machine, while satisfying the requirements of product types, product family, sequence dependent setup time, product-type dependent processing time, due dates of jobs, and capacities of machines. To solve such a complicated problem efficiently and effectively, we adopt the hybrid genetic algorithm approach, which generates initial population with the insertion and savings algorithms for WPSP and provide a new crossover operator to generate better solutions. Considering the disturbance of due dates of jobs in scheduling, we design a new crossover which makes each machine keep the critical sub-schedule in the generation of offspring. The performance of the proposed hybrid genetic algorithm is evaluated by two real-world problems and the solution quality of the proposed approach is analyzed under five levels of mutation rates and four levels of number of generations. Computational results reveal that the hybrid genetic algorithm can efficiently solve the considered problem and the reduction of makespan is related to the mutation rates and the number of generations.

Nonlinear channel blind equalization using hybrid genetic algorithm with simulated annealing

In this paper, a hybrid genetic algorithm, which merges a genetic algorithm with simulated annealing, is presented to solve nonlinear channel blind equalization problems. The equalization of nonlinear channels is more complicated than linear channels, but it is of more practical use in real world environments. The proposed hybrid genetic algorithm with simulated annealing is used to estimate the output states of a nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. By using the desired channel states derived from these estimated output states of the nonlinear channel, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm (GA) and a simplex GA. In particular, we observe a relatively high accuracy and fast convergence of the method.

An order dispatcher for dispersed manufacturers solved by a mixed-variable hybrid genetic algorithm

This paper describes a new solution for an order dispatching problem (ODP) within a distributed manufacturing environment. A mathematical model has been developed to describe the ODP, including both assigning orders to manufacturers and determining the completion times while satisfying a set of constraints. This model has two types of variables: 0-1 decision variables and integer variables that make it a non-linear mixed-variables integer programming model. The usage rate of parts inventory is taken into consideration to avoid parts devaluation. To solve the problem, a novel hybrid genetic algorithm (GA) with a new evolution strategy has been introduced. The hybrid GA can provide more survival chances in the evolving competition for outstanding individuals and introduce potentially excellent individuals to the next generation. To find the appropriate completion times for each order, the hybrid GA embeds a mini-scheduling algorithm to arrange the executing sequence of orders for each manufacturer. The computation results have shown that the proposed hybrid GA can find more satisfactory solutions than the GA with traditional evolution strategies, particularly as the problem size increases with the number of orders.

HYBRID GENETIC ALGORITHMS FOR MINIMIZING THE MAXIMUM COMPLETION TIME FOR THE WAFER PROBING SCHEDULING PROBLEM

ABSTRACT The wafer probing scheduling problem (WPSP) is a variation of the parallel-machine problem, which carries with the objective to minimize the total workload to enhance the utilization rate of machine capacity. However, to minimize the maximum completion time for the wafer probing scheduling problem (WPSP) is very important for equally utilizing the capacity of parallel-machine, while satisfying the requirements of product types, product family, sequence dependent setup time, product-type dependent processing time, due dates of jobs, and capacities of machines. To solve such a complicated problem efficiently and effectively, we adopt the hybrid genetic algorithm approach, which generates initial population with the insertion and savings algorithms for WPSP and provide a new crossover operator to generate better solutions. Considering the disturbance of due dates of jobs in scheduling, we design a new crossover which makes each machine keep the critical sub-schedule in the generation of offspring. The performance of the proposed hybrid genetic algorithm is evaluated by two real-world problems and the solution quality of the proposed approach is analyzed under five levels of mutation rates and four levels of number of generations. Computational results reveal that the hybrid genetic algorithm can efficiently solve the considered problem and the reduction of makespan is related to the mutation rates and the number of generations.

A New Hybrid Genetic Algorithm for Nonlinear Channel Blind Equalization

In this study, a hybrid genetic algorithm merged with simulated annealing is presented to solve nonlinear channel blind equalization problems. The equalization of nonlinear channels is more complicated one, but it is of more practical use in real world environments. The proposed hybrid genetic algorithm with simulated annealing is used to estimate the output states of nonlinear channel, based on the Bayesian likelihood fitness function, instead of the channel parameters. By using the desired channel states derived from these estimated output states of the nonlinear channel, the Bayesian equalizer is implemented to reconstruct transmitted symbols. In the simulations, binary signals are generated at random with Gaussian noise. The performance of the proposed method is compared with those of a conventional genetic algorithm(GA) and a simplex GA. In particular, we observe a relatively high accuracy and fast convergence of the method.

Linkage-shredding genetic algorithm for reliability assessment of structural systems

The identification of the probabilistically dominant failure modes of complex structures and the estimation of their reliability can be reduced to the solution of an optimization problem in the standardized normal space of the variables that control the random loads applied on the structure and the random strengths of its members. Hence, the ability of genetic algorithms to identify local and global optima makes them especially suitable for solving structural reliability problems. To reduce the known inefficiencies of traditional genetic algorithms, this paper proposes the use of a hybrid genetic search algorithm that is capable of efficiently identifying the dominant failure modes of a structural system and estimating their reliability index values. The proposed algorithm combines the benefits and the efficiency of the linkage learning process of the gene expression messy genetic algorithm (GEMGA) to the ability of the shredding genetic operator to explore new significant search domains. By reducing the dimensionality of the problem through linkage learning, the number of generations needed to reach convergence is greatly reduced. To further ameliorate the GA search’s efficiency, the Shredding operator is used to estimate the value of the reliability index in a given search direction by building and updating a fitness value matrix based on an evolutionary learning process. This will drastically reduce the number of structural analyses that are usually required during the reliability assessment of structural systems. Furthermore, an exploitation process is implemented during the search for the local optima to obtain accurate reliability indexes and quantify the contributions of various variables to the structural failure modes identified during the search process. Thus, the proposed algorithm provides detailed information about a structure’s failure modes that would be helpful for optimizing the design and improving the structure’s safety against local and global failures. Examples are provided to demonstrate the high efficiency and accuracy of the proposed hybrid genetic algorithm.

역복사 해석을 위한 혼합형 유전 알고리듬에 관한 연구

An inverse radiation analysis is presented for the estimation of the boundary emissivities for an absorbing, emitting, and scattering media with diffusely emitting and reflecting opaque boundaries. The finite-volume method is employed to solve the radiative transfer equation for a two-dimensional irregular geometry. A hybrid genetic algorithm is proposed for improving the efficiency of the genetic algorithm and reducing the effects of genetic parameters on the performance of the genetic algorithm. After verifying the performance of the proposed hybrid genetic algorithm, it is applied to inverse radiation analysis in estimating the wall emissivities in a two-dimensional irregular medium when the measured temperatures are given at only four data positions. The effect of measurement errors on the estimation accuracy is examined.

Advanced scheduling problem using constraint programming techniques in SCM environment

In this paper we consider a preemptive and non-preemptive scheduling model as one of the advanced scheduling problems considered by a constraint programming technique and propose a new genetic algorithm (GA) considering simultaneously the preemptive and non-preemptive cases of the activities of jobs under single machine job-shop scheduling problems. In the proposed GA, we develop a new method for representing the chromosome of GA. For various comparisons, we also propose the hybrid GAs with the proposed GA and conventional sequencing rules. These hybrid GAs are applied to several job-shop scheduling problems under a single machine. Experiment results show that the proposed hybrid GA with a conventional sequencing rule outperforms the proposed GA and other conventional sequencing rules.

Fish Catching by Robot Using Gazing GA Visual Servoing.

This paper presents a new method of scene recognition for manipulator real-time visual servoing, which utilizes a hybrid genetic algorithm (GA) in combination with a model shaping a target of known shape, and the unprocessed gray-scale image of a scene. The scene recognition method presented here is concerned with the simultaneous recognition of the shape and detection of the position and orientation in the two-dimensional raw-image, of a three-dimensional target being imaged. The proposed hybrid GA employs the global search feature of a two-point crossover of a GA, to search a target, together with a GA-based local search that focuses on the target of interest found so far, in order to detect accurate target's position in a short time by intensive searching. In order to appraise the proposed hybrid GA recognition method, experiments to pick up a natural fish swimming in a pool by hand net of a robot manipulator by using the visual servoing, have been conducted to show the performances with respect to recognition accuracy in time response and the real-time feature.

Investigation of a Practical Method of Structural Optimization by Genetic Algorithms

A new hybrid genetic algorithm (GA) and a parallel genetic algorithm are proposed in this paper in order to improve the convergence of structural optimization. The fully stressed design, FSD, method and a GA were incorporated into the hybrid GA. In the parallel GA, the “island model” was applied to reduce the computation time. Stress analyses were carried out with a commercial FEM code to obtain the optimum solution with high reliability even in a complex model. As a result of the axi-symmetric shell model, the optimum solution by using the proposed hybrid GA was 50% lighter than that by using simple GA. Furthermore, the computation time using the parallel GA with 10 processors was reduced by up to 1/7 of the time using one processor. These methods were then used to optimize the thickness of a three-dimensional shell model of a CRT. As a result of the application, the weight of the optimum model was 25% lighter than that of the reference model.

Genetic Algorithm Based Mixed Spectrum Estimation for Discrimination of Sinusoids and ARMA Noise

This paper is concerned with a method for estimating a mixed spectrum which is composed of multiple sinusoids and an ARMA noise with continuous spectrum. In order to avoid simultaneous minimization of a prediction error criterion with respect to all unknown model parameters, we give an efficient iterative algorithm for estimating the frequencies of the sinusoids and other parameters separately. By adopting the genetic algorithm to choose initial values of the ARMA parameters in the iterative estimation, we can attain a globally optimal estimates of unknown parameters. For executing the proposed hybrid genetic algorithm, a modification of the Toeplitz approximation method is given to estimate the frequencies in the presence of the MA noise, by using a shifted correlation matrix of the filtered observed data. The effectiveness of the proposed algorithm is validated in numerical simulations using the bench mark test data.