Minimal Generation Gap Research Articles

LibRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models

Kinetic modeling is a powerful tool to understand how a biochemical system behaves as a whole. To develop a realistic and predictive model, kinetic parameters need to be estimated so that a model fits experimental data. However, parameter estimation remains a major bottleneck in kinetic modeling. To accelerate parameter estimation, we developed a C library for real-coded genetic algorithms (libRCGA). In libRCGA, two real-coded genetic algorithms (RCGAs), viz. the Unimodal Normal Distribution Crossover with Minimal Generation Gap (UNDX/MGG) and the Real-coded Ensemble Crossover star with Just Generation Gap (REX star/JGG), are implemented in C language and paralleled by Message Passing Interface (MPI). We designed libRCGA to take advantage of high-performance computing environments and thus to significantly accelerate parameter estimation. Constrained optimization formulation is useful to construct a realistic kinetic model that satisfies several biological constraints. libRCGA employs stochastic ranking to efficiently solve constrained optimization problems. In the present paper, we demonstrate the performance of libRCGA through benchmark problems and in realistic parameter estimation problems. libRCGA is freely available for academic usage at http://kurata21.bio.kyutech.ac.jp/maeda/index.html.

A novel attribute reduction algorithm based on rough set and improved artificial fish swarm algorithm

Attribute Reduction (AR) is an important preprocessing step for data mining. AR based on rough set is an efficient method. Its reduction performance has been verified to be better or comparable with other methods in large amount of works, but existing reduction algorithms have some problems such as slow convergent speed and probably converging to a local optimum. A novel attribute reduction algorithm based on Artificial Fish Swarm Algorithm (AFSA) and rough set is proposed. For AFSA has a slow convergence rate in the later phase of iterations, normal distribution function, Cauchy distribution function, multi-parent crossover operator, mutation operator and modified minimal generation gap model are adopted to improve AFSA. The attribute reduction algorithm based on improved AFSA and rough set takes full advantages of the improved AFSA and rough set,which are faster, more efficient, simpler, and easier to be implemented. Datasets in the UC Irvine (UCI) Machine Learning Repository are selected to verify the aforementioned new method. The results show that above algorithm can search the attribute reduction set effectively, and it has low time complexity and the excellent global search ability.

Optimization of energy saving device combined with a propeller using real-coded genetic algorithm

This paper presents a numerical optimization method to improve the performance of the propeller with Turbo-Ring using real-coded genetic algorithm. In the presented method, Unimodal Normal Distribution Crossover (UNDX) and Minimal Generation Gap (MGG) model are used as crossover operator and generation-alternation model, respectively. Propeller characteristics are evaluated by a simple surface panel method “SQCM” in the optimization process. Blade sections of the original Turbo-Ring and propeller are replaced by the NACA66 a = 0.8 section. However, original chord, skew, rake and maximum blade thickness distributions in the radial direction are unchanged. Pitch and maximum camber distributions in the radial direction are selected as the design variables. Optimization is conducted to maximize the efficiency of the propeller with Turbo-Ring. The experimental result shows that the efficiency of the optimized propeller with Turbo-Ring is higher than that of the original propeller with Turbo-Ring.

Efficient Numerical Optimization Algorithm Based on New Real-Coded Genetic Algorithm, AREX + JGG, and Application to the Inverse Problem in Systems Biology

In Systems Biology, system identification, which infers regulatory network in genetic system and metabolic pathways using experimentally observed time-course data, is one of the hottest issues. The efficient numerical optimization algorithm to estimate more than 100 real-coded parameters should be developed for this purpose. New real-coded genetic algorithm (RCGA), the combination of AREX (adaptive real-coded ensemble crossover) with JGG (just generation gap), have applied to the inference of genetic interactions involving more than 100 parameters related to the interactions with using experimentally observed time-course data. Compared with conventional RCGA, the combination of UNDX (unimodal normal distribution crossover) with MGG (minimal generation gap), new algorithm has shown the superiority with improving early convergence in the first stage of search and suppressing evolutionary stagnation in the last stage of search.

プログラムサイズを考慮した進化アルゴリズムによるグラフ構造状プログラムの自動生成

Today, a lot of automatic programming techniques have been proposed and applied in various fields. Graph Structured Program Evolution (GRAPE) is one of the recent automatic programming techniques. The representation of GRAPE is graph structure, therefore it can represent complex programs using its graph structure. GRAPE succeeds in generating the complex programs automatically. The generation alternation model of GRAPE is usually used minimal generation gap (MGG) which is not considered the evolution of program size. Therefore, it would not be search various program sizes. In this paper, a new evolutionary algorithm for GRAPE, called Evolutionary Algorithm Considering Program Size (EACP), is proposed. EACP maintains the diversity of program size in the population by using particular fitness assignment and generation alternation. We apply EACP to three test problems, factorial, exponentiation and sorting a list. And we show the effectiveness of EACP and confirm evolution of maintaining the diversity of program size.

Analysis of The Behavior of MGG and JGG As A Selection Model for Real-coded Genetic Algorithms

In this paper, we focus on analyzing the behavior of the selection models for real-coded genetic algorithms. Recent studies show that Just Generation Gap (JGG) selection model outperforms Minimal Generation Gap (MGG) model when a multi-parental crossover operator based on the hypothesis of the preservation of the statistics of parents is used. However, the validation of JGG selection model is not done yet. To validate the selection method of JGG, we analyze the differences of the behavior of JGG selection model and that of MGG selection model.

家族横断選択を取り入れたＭＧＧの提案とＤＳ‐ＧＡへの適用による解探索性能の向上

MGG (Minimal Generation Gap) is one of popular generation alternation models for Genetic Algorithms (GAs). The conventional MGG is effective for single population GAs, but not for multi-population GAs. This paper proposes ``MGG with global selection (MGGGS)'' that is designed for multi-population GAs. In MGGGS, the selection operation is carried out through the whole population, while the crossover operation is restricted in sub-populations. Experiments are carried out to analyze the characteristics of MGGGS with Dynamically Separating Genetic Algorithm (DS-GA). In DS-GA the sub-populations are reconstructed during the evolution, which is suitable for MGGGS. The experimental results show that MGGGS outperforms the conventional MGG especially for multimodal functions, since sub-populations explore various areas by MGGGS.

Saving MGG: Reducing Fitness Evaluations for Real-coded GA/MGG.

In this paper, we propose an extension of the Minimal Generation Gap (MGG) to reduce the number of fitness evaluation for the real-coded GAs (RCGA). When MGG is applied to actual engineering problems, for example applied to optimization of design parameters, the fitness calculating time is usually huge because MGG generates many children from one pair of parents and the fitness is calculated by repetitive simulation or analysis. The proposed method called Saving MGG reduces the number of fitness evaluation by estimating the promising degrees of children using individual distribution and fitness information of population, and selecting children based on the promising degree before evaluating the fitness. Experimental results show that RCGA with Saving MGG can provide large reducing effects on 20 or 30 dimensional Sphere functions, Rosenbrock functions, ill-scaled Rosenbrock functions, and Rastrigin function.

Laser field designing by evolutionary algorithm for coherent control of molecular dynamics

A versatile method for theoretical designing of laser pulses for coherent control of molecular dynamics based on evolutionary algorithm is presented. Extended normal distribution crossover (ENDX) together with minimal generation gap (MGG) is employed for laser parameter optimization. The method is applied to the selective excitation problem of isotope molecules.

A neurocomputing model for real coded genetic algorithm with the minimal generation gap

This paper proposes using neural networks (NN) to implement a real coded genetic algorithm (GA) with the center of gravity crossover (CGX) and the minimal generation gap (MGG) model. With all genetic operations of GA including selection, crossover, mutation and evaluation implemented with NN modules, this approach can realize in parallel genetic operations on the whole chromosome to achieve the maximum parallel realization potential of the MGG model of the GA. At the same time expensive hardware for field programmable gate arrays (FPGA) and the high speed memory of hardware for GA can be avoided. The performance of our solution is validated with a suite of benchmark test functions. This paper suggests that implementing GA with NN is a promising research direction for greatly reducing the running time of GA.

An improved genetic algorithm for planar and spatial straightness error evaluation

The measurement data obtained from the Coordinate Measuring Machines (CMMs) have to be further processed and analyzed to evaluate the form errors of manufactured components. An improved genetic algorithm (GA) is proposed to implement the minimum zone evaluation of planar and spatial straightness errors simultaneously. The algorithm employs the generation alternation model based Minimal Generation Gap (MGP) and blend crossover operators (BLX- α). Compared to traditional GAs, it is efficient and robust. Then, the objective function calculation approaches of planar and spatial straightness error are developed, which directly originate from the definition of minimum zone solution and conform to the ISO standard. Finally, the experimental results evaluated by different methods confirm the effectiveness of the proposed GA. Compared to conventional evaluation methods; it has the advantages of algorithm simplicity and good flexibility. Also it is a unified approach for other form error evaluations and is well suited for the form error evaluation on CMMs.

Integrated System for Inference of Gene Expression Network

Recent advances of powerful new technologies such as DNA microarrays provide a mass of gene expres-sion data on a genomic scale. One of the most important projects in post-genome-era is the systemidenti cation of gene expression networks by using these observed data.We previously proposed an ecient numerical optimization technique by using time-course dataof system components, which is based on real-coded genetic algorithm (RCGAs [1, 2]) to estimate theinterrelatedcoecients amongsystemcomponentsofadynamicnetworkmodelcalledS-system[3]thatis a type of power-low formalism and is suitable for description of organizationally complex systemssuch as gene expression networks and metabolic pathways. This technique with the combinationof unimodal normal distribution crossover (UNDX) [1], which is one of the crossover operators forRCGAs, with the alternation of generation model called minimal generation gap (MGG) [2] showedsuperiority to the conventional simple genetic algorithm [4, 5].In this study we shall describe on the integrated inferring system involving the GUI programfor real-time visualization of the inferred network structures by using distributed parallel computersystems.