Search results for: Genetic regulatory network

Authors: Anis Gharbi, Mohamed Haouari, Talel Ladhari, Mohamed Ali Rakrouki

Abstract:

We address a complex scheduling problem arising in the wood panel industry with the objective of minimizing a quadratic function of job tardiness. The proposed solution strategy, which is based on an effective genetic algorithm, has been coded and implemented within a major Tunisian company, leader in the wood panel manufacturing. Preliminary experimental results indicate significant decrease of delivery times.

Keywords: Genetic algorithm, heuristic, hybrid flowshop, total weighted squared tardiness.

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Authors: M. Soryani, N. Rafat

Abstract:

Dealing with hundreds of features in character recognition systems is not unusual. This large number of features leads to the increase of computational workload of recognition process. There have been many methods which try to remove unnecessary or redundant features and reduce feature dimensionality. Besides because of the characteristics of Farsi scripts, it-s not possible to apply other languages algorithms to Farsi directly. In this paper some methods for feature subset selection using genetic algorithms are applied on a Farsi optical character recognition (OCR) system. Experimental results show that application of genetic algorithms (GA) to feature subset selection in a Farsi OCR results in lower computational complexity and enhanced recognition rate.

Keywords: Feature Subset Selection, Genetic Algorithms, Optical Character Recognition.

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Authors: Basheer M. Al-Maqaleh, Kamal K. Bharadwaj

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Automated discovery of hierarchical structures in large data sets has been an active research area in the recent past. This paper focuses on the issue of mining generalized rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses flat rules as initial individuals of GP and discovers hierarchical structure. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy. Experimental results are presented to demonstrate the performance of the proposed algorithm.

Keywords: Genetic Programming, Hierarchy, Knowledge Discovery in Database, Subsumption Matrix.

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Authors: Zhenhuan Zhu, David Mulvaney, Vassilios Chouliaras

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A new genetic algorithm, termed the 'optimum individual monogenetic genetic algorithm' (OIMGA), is presented whose properties have been deliberately designed to be well suited to hardware implementation. Specific design criteria were to ensure fast access to the individuals in the population, to keep the required silicon area for hardware implementation to a minimum and to incorporate flexibility in the structure for the targeting of a range of applications. The first two criteria are met by retaining only the current optimum individual, thereby guaranteeing a small memory requirement that can easily be stored in fast on-chip memory. Also, OIMGA can be easily reconfigured to allow the investigation of problems that normally warrant either large GA populations or individuals many genes in length. Local convergence is achieved in OIMGA by retaining elite individuals, while population diversity is ensured by continually searching for the best individuals in fresh regions of the search space. The results given in this paper demonstrate that both the performance of OIMGA and its convergence time are superior to those of a range of existing hardware GA implementations.

Keywords: Genetic algorithms, genetic hardware, machinelearning.

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Authors: Mazdak Zamani , Azizah A. Manaf , Rabiah B. Ahmad , Akram M. Zeki , Shahidan Abdullah

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In this paper, we present a novel, principled approach to resolve the remained problems of substitution technique of audio steganography. Using the proposed genetic algorithm, message bits are embedded into multiple, vague and higher LSB layers, resulting in increased robustness. The robustness specially would be increased against those intentional attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well.

Keywords: Artificial Intelligence, Audio Steganography, DataHiding, Genetic Algorithm, Substitution Techniques.

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Authors: N. Moghadam, H. Sadeghi

Abstract:

In this paper a novel scheme for watermarking digital audio during its compression to MPEG-1 Layer III format is proposed. For this purpose we slightly modify some of the selected MDCT coefficients, which are used during MPEG audio compression procedure. Due to the possibility of modifying different MDCT coefficients, there will be different choices for embedding the watermark into audio data, considering robustness and transparency factors. Our proposed method uses a genetic algorithm to select the best coefficients to embed the watermark. This genetic selection is done according to the parameters that are extracted from the perceptual content of the audio to optimize the robustness and transparency of the watermark. On the other hand the watermark security is increased due to the random nature of the genetic selection. The information of the selected MDCT coefficients that carry the watermark bits, are saves in a database for future extraction of the watermark. The proposed method is suitable for online MP3 stores to pursue illegal copies of musical artworks. Experimental results show that the detection ratio of the watermarks at the bitrate of 128kbps remains above 90% while the inaudibility of the watermark is preserved.

Keywords: Content-Based Audio Watermarking, Genetic AudioWatermarking.

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Authors: Elsheikh Asser

Abstract:

Time and cost are the main goals of the construction project management. The first schedule developed may not be a suitable schedule for beginning or completing the project to achieve the target completion time at a minimum total cost. In general, there are trade-offs between time and cost (TCT) to complete the activities of a project. This research presents genetic algorithms (GAs) multiobjective model for project scheduling considering different scenarios such as least cost, least time, and target time.

Keywords: Genetic algorithms, Time-cost trade-off.

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Authors: Abu Qudeiri Jaber, Yamamoto Hidehiko Rizauddin Ramli

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To create a solution for a specific problem in machine learning, the solution is constructed from the data or by use a search method. Genetic algorithms are a model of machine learning that can be used to find nearest optimal solution. While the great advantage of genetic algorithms is the fact that they find a solution through evolution, this is also the biggest disadvantage. Evolution is inductive, in nature life does not evolve towards a good solution but it evolves away from bad circumstances. This can cause a species to evolve into an evolutionary dead end. In order to reduce the effect of this disadvantage we propose a new a learning tool (criteria) which can be included into the genetic algorithms generations to compare the previous population and the current population and then decide whether is effective to continue with the previous population or the current population, the proposed learning tool is called as Keeping Efficient Population (KEP). We applied a GA based on KEP to the production line layout problem, as a result KEP keep the evaluation direction increases and stops any deviation in the evaluation.

Keywords: Genetic algorithms, Layout problem, Machinelearning, Production system.

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Authors: N. Kumaresan, J. Kavikumar, M. Kumudthaa, Kuru Ratnavelu

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In this paper, solution of fuzzy differential equation under general differentiability is obtained by genetic programming (GP). The obtained solution in this method is equivalent or very close to the exact solution of the problem. Accuracy of the solution to this problem is qualitatively better. An illustrative numerical example is presented for the proposed method.

Keywords: Fuzzy differential equation, Generalized differentiability, Genetic programming and H-difference.

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Authors: Mona Heydari, Ehsan Motamedian, Seyed Abbas Shojaosadati

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Prediction of perturbations after genetic manipulation (especially gene knockout) is one of the important challenges in systems biology. In this paper, a new algorithm is introduced that integrates microarray data into the metabolic model. The algorithm was used to study the change in the cell phenotype after knockout of Gss gene in Escherichia coli BW25113. Algorithm implementation indicated that gene deletion resulted in more activation of the metabolic network. Growth yield was more and less regulating gene were identified for mutant in comparison with the wild-type strain.

Keywords: Metabolic network, gene knockout, flux balance analysis, microarray data, integration.

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Authors: A. R. Malekpour, A.R. Seifi

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This paper discusses a genetic algorithm (GA) based optimal load shedding that can apply for electrical distribution networks with and without dispersed generators (DG). Also, the proposed method has the ability for considering constant and variable capacity deficiency caused by unscheduled outages in the bulked generation and transmission system of bulked power supply. The genetic algorithm (GA) is employed to search for the optimal load shedding strategy in distribution networks considering DGs in two cases of constant and variable modelling of bulked power supply of distribution networks. Electrical power distribution systems have a radial network and unidirectional power flows. With the advent of dispersed generations, the electrical distribution system has a locally looped network and bidirectional power flows. Therefore, installed DG in the electrical distribution systems can cause operational problems and impact on existing operational schemes. Introduction of DGs in electrical distribution systems has introduced many new issues in operational and planning level. Load shedding as one of operational issue has no exempt. The objective is to minimize the sum of curtailed load and also system losses within the frame-work of system operational and security constraints. The proposed method is tested on a radial distribution system with 33 load points for more practical applications.

Keywords: DG, Load shedding, Optimization, Capacity Deficiency Modelling.

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Authors: Serkawt Khola

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A feature weighting and selection method is proposed which uses the structure of a weightless neuron and exploits the principles that govern the operation of Genetic Algorithms and Evolution. Features are coded onto chromosomes in a novel way which allows weighting information regarding the features to be directly inferred from the gene values. The proposed method is significant in that it addresses several problems concerned with algorithms for feature selection and weighting as well as providing significant advantages such as speed, simplicity and suitability for real-time systems.

Keywords: Feature weighting, genetic algorithm, pattern recognition, weightless neuron.

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Authors: Jamuna Bhandari

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String matching also known as pattern matching is one of primary concept for network security. In this area the effectiveness and efficiency of string matching algorithms is important for applications in network security such as network intrusion detection, virus detection, signature matching and web content filtering system. This paper presents brief review on some of string matching techniques used for network security.

Keywords: Filtering, honeypot, network telescope, pattern, string, signature.

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Authors: Mohammad Al-Fayoumi, Soumya Banerjee, Jr., P. K. Mahanti

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A social network is a set of people or organization or other social entities connected by some form of relationships. Analysis of social network broadly elaborates visual and mathematical representation of that relationship. Web can also be considered as a social network. This paper presents an innovative approach to analyze a social network using a variant of existing ant colony optimization algorithm called as Clever Ant Colony Metaphor. Experiments are performed and interesting findings and observations have been inferred based on the proposed model.

Keywords: Social Network, Ant Colony, Maximum Clique, Sub graph, Clever Ant colony.

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Authors: Doru Anastasiu Popescu, Dan Rădulescu

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In this paper, we determine the similarity of two HTML web applications. We are going to use a genetic algorithm in order to determine the most significant web pages of each application (we are not going to use every web page of a site). Using these significant web pages, we will find the similarity value between the two applications. The algorithm is going to be efficient because we are going to use a reduced number of web pages for comparisons but it will return an approximate value of the similarity. The binary trees are used to keep the tags from the significant pages. The algorithm was implemented in Java language.

Keywords: Tag, HTML, web page, genetic algorithm, similarity value, binary tree.

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Authors: Halil Ibrahim Demir, Alper Goksu, Onur Canpolat, Caner Erden, Melek Nur

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Traditionally process planning, scheduling and due date assignment are performed sequentially and separately. High interrelation between these functions makes integration very useful. Although there are numerous works on integrated process planning and scheduling and many works on scheduling with due date assignment, there are only a few works on the integration of these three functions. Here we tested the different integration levels of these three functions and found a fully integrated version as the best. We applied genetic search and random search and genetic search was found better compared to the random search. We penalized all earliness, tardiness and due date related costs. Since all these three terms are all undesired, it is better to penalize all of them.

Keywords: Process planning, scheduling, due-date assignment, genetic algorithm, random search.

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Authors: Tayfun Çay, Yaşar İnceyol, Abdurrahman Özbeyaz

Abstract:

Land reallocation is one of the most important steps in land consolidation projects. Many different models were proposed for land reallocation in the literature such as Fuzzy Logic, block priority based land reallocation and Spatial Decision Support Systems. A model including four parts is considered for automatic block reallocation with genetic algorithm method in land consolidation projects. These stages are preparing data tables for a project land, determining conditions and constraints of land reallocation, designing command steps and logical flow chart of reallocation algorithm and finally writing program codes of Genetic Algorithm respectively. In this study, we designed the first three steps of the considered model comprising four steps.

Keywords: Genetic algorithm, land consolidation, landholding, land reallocation.

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Authors: O.M. Mohamed vall, M. Radhi

Abstract:

This work presents a recursive identification algorithm. This algorithm relates to the identification of closed loop system with Variable Structure Controller. The approach suggested includes two stages. In the first stage a genetic algorithm is used to obtain the parameters of switching function which gives a control signal rich in commutations (i.e. a control signal whose spectral characteristics are closest possible to those of a white noise signal). The second stage consists in the identification of the system parameters by the instrumental variable method and using the optimal switching function parameters obtained with the genetic algorithm. In order to test the validity of this algorithm a simulation example is presented.

Keywords: Closed loop identification, variable structure controller, pseud-random binary sequence, genetic algorithms.

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Authors: Han Yu, Dan C. Marinescu, Annie S. Wu, Howard Jay Siegel

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In this paper, we introduce an effective strategy for subgoal division and ordering based upon recursive subgoals and combine this strategy with a genetic-based planning approach. This strategy can be applied to domains with conjunctive goals. The main idea is to recursively decompose a goal into a set of serializable subgoals and to specify a strict ordering among the subgoals. Empirical results show that the recursive subgoal strategy reduces the size of the search space and improves the quality of solutions to planning problems.

Keywords: Planning, recursive subgoals, Sliding-tile puzzle, subgoal interaction, genetic algorithms.

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Authors: D.Venkatesan, K.Kannan, S. Raja Balachandar

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In this paper a new Genetic Algorithm based on a heuristic operator and Centre of Mass selection operator (CMGA) is designed for the unbounded knapsack problem(UKP), which is NP-Hard combinatorial optimization problem. The proposed genetic algorithm is based on a heuristic operator, which utilizes problem specific knowledge. This center of mass operator when combined with other Genetic Operators forms a competitive algorithm to the existing ones. Computational results show that the proposed algorithm is capable of obtaining high quality solutions for problems of standard randomly generated knapsack instances. Comparative study of CMGA with simple GA in terms of results for unbounded knapsack instances of size up to 200 show the superiority of CMGA. Thus CMGA is an efficient tool of solving UKP and this algorithm is competitive with other Genetic Algorithms also.

Keywords: Genetic Algorithm, Unbounded Knapsack Problem, Combinatorial Optimization, Meta-Heuristic, Center of Mass

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Authors: P. Cermak

Abstract:

This paper shows possibility of extraction Social, Group and Individual Mind from Multiple Agents Rule Bases. Types those Rule bases are selected as two fuzzy systems, namely Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are describing (modeling) agent behavior. Modifying of agent behavior in the time varying environment will be provided by learning fuzzyneural networks and optimization of their parameters with using genetic algorithms in development system FUZNET. Finally, extraction Social, Group and Individual Mind from Multiple Agents Rule Bases are provided by Cognitive analysis and Matching criterion.

Keywords: Mind, Multi-agent system, Cognitive analysis, Fuzzy system, Neural network, Genetic algorithm, Rule base.

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Authors: Muhammad Naeem, Syed Ismail Shah, Habibullah Jamal

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In this paper, a simple heuristic genetic algorithm is used for Multistage Multiuser detection in fast fading environments. Multipath channels, multiple access interference (MAI) and near far effect cause the performance of the conventional detector to degrade. Heuristic Genetic algorithms, a rapidly growing area of artificial intelligence, uses evolutionary programming for initial search, which not only helps to converge the solution towards near optimal performance efficiently but also at a very low complexity as compared with optimal detector. This holds true for Additive White Gaussian Noise (AWGN) and multipath fading channels. Experimental results are presented to show the superior performance of the proposed techque over the existing methods.

Keywords: Genetic Algorithm (GA), Multiple AccessInterference (MAI), Multistage Detectors (MSD), SuccessiveInterference Cancellation.

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Authors: E. Dolatdar, S. Soleymani, B. Mozafari

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Power loss reduction is one of the main targets in power industry and so in this paper, the problem of finding the optimal configuration of a radial distribution system for loss reduction is considered. Optimal reconfiguration involves the selection of the best set of branches to be opened ,one each from each loop, for reducing resistive line losses , and reliving overloads on feeders by shifting the load to adjacent feeders. However ,since there are many candidate switching combinations in the system ,the feeder reconfiguration is a complicated problem. In this paper a new approach is proposed based on a simple optimum loss calculation by determining optimal trees of the given network. From graph theory a distribution network can be represented with a graph that consists a set of nodes and branches. In fact this problem can be viewed as a problem of determining an optimal tree of the graph which simultaneously ensure radial structure of each candidate topology .In this method the refined genetic algorithm is also set up and some improvements of algorithm are made on chromosome coding. In this paper an implementation of the algorithm presented by [7] is applied by modifying in load flow program and a comparison of this method with the proposed method is employed. In [7] an algorithm is proposed that the choice of the switches to be opened is based on simple heuristic rules. This algorithm reduce the number of load flow runs and also reduce the switching combinations to a fewer number and gives the optimum solution. To demonstrate the validity of these methods computer simulations with PSAT and MATLAB programs are carried out on 33-bus test system. The results show that the performance of the proposed method is better than [7] method and also other methods.

Keywords: Distribution System, Reconfiguration, Loss Reduction , Graph Theory , Optimization , Genetic Algorithm

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Authors: A.K.M. Khaled Ahsan Talukder, Taibun Nessa, Kaushik Roy

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The weight constrained shortest path problem (WCSPP) is one of most several known basic problems in combinatorial optimization. Because of its importance in many areas of applications such as computer science, engineering and operations research, many researchers have extensively studied the WCSPP. This paper mainly concentrates on the reduction of total search space for finding WCSP using some existing Genetic Algorithm (GA). For this purpose, some controlled schemes of genetic operators are adopted on list chromosome representation. This approach gives a near optimum solution with smaller elapsed generation than classical GA technique. From further analysis on the matter, a new generalized schema theorem is also developed from the philosophy of Holland-s theorem.

Keywords: Genetic Algorithm, Evolutionary Optimization, Multi Objective Optimization, Non-linear Schema Theorem, WCSPP.

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Authors: Muqdad Al-Juboori, Bithin Datta

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Hydraulic structures such as gravity dams are classified as essential structures, and have the vital role in providing strong and safe water resource management. Three major aspects must be considered to achieve an effective design of such a structure: 1) The building cost, 2) safety, and 3) accurate analysis of seepage characteristics. Due to the complexity and non-linearity relationships of the seepage process, many approximation theories have been developed; however, the application of these theories results in noticeable errors. The analytical solution, which includes the difficult conformal mapping procedure, could be applied for a simple and symmetrical problem only. Therefore, the objectives of this paper are to: 1) develop a surrogate model based on numerical simulated data using SEEPW software to approximately simulate seepage process related to a hydraulic structure, 2) develop and solve a linked simulation-optimization model based on the developed surrogate model to describe the seepage occurring under a concrete gravity dam, in order to obtain optimum and safe design at minimum cost. The result shows that the linked simulation-optimization model provides an efficient and optimum design of concrete gravity dams.

Keywords: Artificial neural network, concrete gravity dam, genetic algorithm, seepage analysis.

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Authors: Z. Pavlík, J. Žumár, M. Pavlíková, J. Kočí, R. Černý

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Water vapour transport properties of gypsum block are studied in dependence on relative humidity using inverse analysis based on genetic algorithm. The computational inverse analysis is performed for the relative humidity profiles measured along the longitudinal axis of a rod sample. Within the performed transient experiment, the studied sample is exposed to two environments with different relative humidity, whereas the temperature is kept constant. For the basic gypsum characterisation and for the assessment of input material parameters necessary for computational application of genetic algorithm, the basic material properties of gypsum are measured as well as its thermal and water vapour storage parameters. On the basis of application of genetic algorithm, the relative humidity dependent water vapour diffusion coefficient and water vapour diffusion resistance factor are calculated.

Keywords: Water vapour transport, gypsum block, transient experiment, genetic algorithm.

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Authors: T. Hussein

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In this paper, determining the optimal proportionalintegral- derivative (PID) controller gains of an single-area load frequency control (LFC) system using genetic algorithm (GA) is presented. The LFC is notoriously difficult to control optimally using conventionally tuning a PID controller because the system parameters are constantly changing. It is for this reason the GA as tuning strategy was applied. The simulation has been conducted in MATLAB Simulink package for single area power system. the simulation results shows the effectiveness performance of under various disturbance.

Keywords: Load Frequency Control (LFC), PID controller and Genetic Algorithm (GA).

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Authors: A.M. Al-Fahed Nuseirat, R. Abu-Zitar

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In this paper the design of maximally flat linear phase finite impulse response (FIR) filters is considered. The problem is handled with totally two different approaches. The first one is completely deterministic numerical approach where the problem is formulated as a Linear Complementarity Problem (LCP). The other one is based on a combination of Markov Random Fields (MRF's) approach with messy genetic algorithm (MGA). Markov Random Fields (MRFs) are a class of probabilistic models that have been applied for many years to the analysis of visual patterns or textures. Our objective is to establish MRFs as an interesting approach to modeling messy genetic algorithms. We establish a theoretical result that every genetic algorithm problem can be characterized in terms of a MRF model. This allows us to construct an explicit probabilistic model of the MGA fitness function and introduce the Ising MGA. Experimentations done with Ising MGA are less costly than those done with standard MGA since much less computations are involved. The least computations of all is for the LCP. Results of the LCP, random search, random seeded search, MGA, and Ising MGA are discussed.

Keywords: Filter design, FIR digital filters, LCP, Ising model, MGA, Ising MGA.

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Authors: Salwa Hagag Abdelaziz, Dorria Salem, Hoda Zaki, Suzan Atteya

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Early detection of breast cancer saves many thousands of lives each year via application of mammography and genetic screening and many more lives could be saved if nurses are involved in breast care screening practices. So, the aim of the study was to identify nurse's role in early detection of breast cancer through mammography and genetic screening and its impact on patient's outcome. In order to achieve this aim, 400 women above 40 years, asymptomatic were recruited for mammography and genetic screening. In addition, 50 nurses and 6 technologists were involved in the study. A descriptive analytical design was used. Five tools were utilized: sociodemographic, mammographic examination and risk factors, women's before, during and after mammography, items relaying to technologists, and items related to nurses were also obtained. The study finding revealed that 3% of women detected for malignancy and 7.25% for fibroadenoma. Statistically significant differences were found between mammography results and age, family history, genetic screening, exposure to smoke, and using contraceptive pills. Nurses have insufficient knowledge about screening tests. Based on these findings the present study recommended involvement of nurses in breast care which is very important to in force population about screening practices.

Keywords: Early detection, Genetic Screening, Mammography.

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Authors: San Nah Sze, Wei King Tiong

Abstract:

The multiple traveling salesman problem (mTSP) can be used to model many practical problems. The mTSP is more complicated than the traveling salesman problem (TSP) because it requires determining which cities to assign to each salesman, as well as the optimal ordering of the cities within each salesman's tour. Previous studies proposed that Genetic Algorithm (GA), Integer Programming (IP) and several neural network (NN) approaches could be used to solve mTSP. This paper compared the results for mTSP, solved with Genetic Algorithm (GA) and Nearest Neighbor Algorithm (NNA). The number of cities is clustered into a few groups using k-means clustering technique. The number of groups depends on the number of salesman. Then, each group is solved with NNA and GA as an independent TSP. It is found that k-means clustering and NNA are superior to GA in terms of performance (evaluated by fitness function) and computing time.

Keywords: Multiple Traveling Salesman Problem, GeneticAlgorithm, Nearest Neighbor Algorithm, k-Means Clustering.

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