Design of artificial genetic regulatory networks with multiple delayed adaptive responses

Genetic regulatory networks with adaptive responses are widely studied in biology. Usually, models consisting only of a few nodes have been considered. They present one input receptor for activation and one output node where the adaptive response is computed. In this work, we design genetic regulatory networks with many receptors and many output nodes able to produce delayed adaptive responses. This design is performed by using an evolutionary algorithm of mutations and selections that minimizes an error function defined by the adaptive response in signal shapes. We present several examples of network constructions with a predefined required set of adaptive delayed responses. We show that an output node can have different kinds of responses as a function of the activated receptor. Additionally, complex network structures are presented since processing nodes can be involved in several input-output pathways

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

Copyright © 2009 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Environmental Modelling and Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling and Software, Volume 24 Issue 4 (2009), DOI: 10.1016/j.envsoft.2008.09.013This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA-ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the 'full' fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA-ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA-ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution

Intelligent Active Vibration Control for a Flexible Beam System

YesThis paper presents an investigation into the development of an intelligent active vibration control (AVC) system. Evolutionary Genetic algorithms (GAs) and Adaptive Neuro-Fuzzy Inference system (ANFIS) algorithms are used to develop mechanisms of an AVC system, where the controller is designed on the basis of optimal vibration suppression using the plant model. A simulation platform of a flexible beam system in transverse vibration using finite difference (FD) method is considered to demonstrate the capabilities of the AVC system using GAs and ANFIS. MATLAB GA tool box for GAs and Fuzzy Logic tool box for ANFIS function are used for AVC system design. The system is then implemented, tested and its performance assessed for GAs and ANFIS based design. Finally a comparative performance of the algorithm in implementing AVC system using GAs and ANFIS is presented and discussed through a set of experiments

Aerodynamic shape optimization using adaptive remeshing

Adaptive mesh refinement is one of the most important tools in Computational Fluid Dynamics (CFD) for solving complex engineering design problems. The paper investigates two practical transonic aerofoil design optimization problems using a genetic algorithm coupled with an Euler aerodynamic analysis tool. The first problem consists in the minimization of transonic drag whereas the second is a reconstruction transonic problem solved by minimizing the pressure error. In both cases, the solutions obtained with and without adaptive mesh refinement are compared. Numerical results obtained by both drag minimization and reconstruction design clearly show that the use of adaptive mesh refinement reduces the computational cost and also produces a better solution.Postprint (published version

Genetic algorithms for reflective filters design

A genetic algorithm (GA) with adaptive mutations has been employed for the design of Bragg reflectors. The algorithm enables three different design types: (a) composition and thickness of each layer are optimized; (b) two compositions are chosen for the two alternating materials, and the thickness of each layer is optimized; (c) composition and thickness of two layers are chosen and the pair is repeated. The algorithm enables the finding of the optimal design for two chosen incident and final media, and it is capable of taking into account the existence of a finite optically thick substrate. The algorithm is very versatile and can be applied for the design of refractive filters using various materials. We have demonstrated application of this algorithm to the design of AlxGa1-xN reflectors, as well as organic and dielectric reflectors.published_or_final_versio

An Improved Robot Path Planning Algorithm

Robot path planning is a NP problem. Traditionaloptimization methods are not very effective to solve it. Traditional genetic algorithm trapped into the local minimum easily. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal design method then applied it to the population initialization, using the intergenerational elite mechanism, as well as the introduction of adaptive local search operator to prevent trapped into the local minimum and improvethe convergence speed to form a new genetic algorithm. Through the series of numerical experiments, the new algorithm has been proved to be efficiency.We also use the proposed algorithm to solve the robot path planning problem and the experiment results indicated that the new algorithm is efficiency for solving the robot path planning problems and the best path usually can be found

Novel Wide Harmonic Suppression Antenna Designed Using Adaptive Meshing and Genetic Algorithms.

yesMicrostrip patch antennas with harmonic suppression are designed and optimised, using a genetic algorithm and applying a novel adaptive meshing program to generate a wire-grid simulation. A coaxially-fed air-dielectric patch antenna design with a folded patch was investigated. It was confirmed that antennas with excellent performances could be designed by this method.MSCR

A genetic algorithm approach to designing and modelling of a multi-functional fractal manufacturing layout

A dynamic and optimal shop floor design, modelling and implementation is key to achieving successful Fractal Manufacturing System (FrMS). To build adaptive and fault-tolerant fractal layout, attention is paid to issues of shop floor planning, function layout, determination of capacity level, cell composition planning and flow distances of products. A full fledged FrMS. layout is multi-functional and is capable of producing a variety of products with minimal reconfiguration. This paper is part and a progression of an on-going project whereby Genetic Algorithm (GA) is adopted to design and model a flexible and multi-functional FrMS floor layout. GA is used in the project for modeling and simulation. The design implementation is done using MATLAB. The result is a fault tolerant configuration that self-regulates and adapts to unpredictable changes in the manufacturing environment arising from lead time reduction pressure, inventories, product customization and other challenges of a dynamic and volatile operational environment