Search results for: Genetic Evolution
1246 A New Particle Filter Inspired by Biological Evolution: Genetic Filter
Authors: S. Park, J. Hwang, K. Rou, E. Kim
Abstract:In this paper, we consider a new particle filter inspired by biological evolution. In the standard particle filter, a resampling scheme is used to decrease the degeneracy phenomenon and improve estimation performance. Unfortunately, however, it could cause the undesired the particle deprivation problem, as well. In order to overcome this problem of the particle filter, we propose a novel filtering method called the genetic filter. In the proposed filter, we embed the genetic algorithm into the particle filter and overcome the problems of the standard particle filter. The validity of the proposed method is demonstrated by computer simulation.
Keywords: Particle filter, genetic algorithm, evolutionary algorithm.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2355
1245 Machine Learning in Production Systems Design Using Genetic Algorithms
Authors: Abu Qudeiri Jaber, Yamamoto Hidehiko Rizauddin Ramli
Abstract: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.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1495
1244 Intuition Operator: Providing Genomes with Reason
Authors: Grigorios N. Beligiannis, Georgios A. Tsirogiannis, Panayotis E. Pintelas
In this contribution, the use of a new genetic operator is proposed. The main advantage of using this operator is that it is able to assist the evolution procedure to converge faster towards the optimal solution of a problem. This new genetic operator is called ''intuition'' operator. Generally speaking, one can claim that this operator is a way to include any heuristic or any other local knowledge, concerning the problem, that cannot be embedded in the fitness function. Simulation results show that the use of this operator increases significantly the performance of the classic Genetic Algorithm by increasing the convergence speed of its population.
Keywords: Genetic algorithms, intuition operator, reasonable genomes, complex search space, nonlinear fitness functionsProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1457
1243 Genetic Algorithm for Solving Non-Convex Economic Dispatch Problem
Authors: Navid Javidtash, Abdolmohamad Davodi, Mojtaba Hakimzadeh, Abdolreza Roozbeh
Economic dispatch (ED) is considered to be one of the key functions in electric power system operation. This paper presents a new hybrid approach based genetic algorithm (GA) to economic dispatch problems. GA is most commonly used optimizing algorithm predicated on principal of natural evolution. Utilization of chaotic queue with GA generates several neighborhoods of near optimal solutions to keep solution variation. It could avoid the search process from becoming pre-mature. For the objective of chaotic queue generation, utilization of tent equation as opposed to logistic equation results in improvement of iterative speed. The results of the proposed approach were compared in terms of fuel cost, with existing differential evolution and other methods in literature.
Keywords: Economic Dispatch(ED), Optimization, Fuel Cost, Genetic Algorithm (GA).Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2254
1242 A Dynamic Hybrid Option Pricing Model by Genetic Algorithm and Black- Scholes Model
Authors: Yi-Chang Chen, Shan-Lin Chang, Chia-Chun Wu
Abstract:Unlike this study focused extensively on trading behavior of option market, those researches were just taken their attention to model-driven option pricing. For example, Black-Scholes (B-S) model is one of the most famous option pricing models. However, the arguments of B-S model are previously mentioned by some pricing models reviewing. This paper following suggests the importance of the dynamic character for option pricing, which is also the reason why using the genetic algorithm (GA). Because of its natural selection and species evolution, this study proposed a hybrid model, the Genetic-BS model which combining GA and B-S to estimate the price more accurate. As for the final experiments, the result shows that the output estimated price with lower MAE value than the calculated price by either B-S model or its enhanced one, Gram-Charlier garch (G-C garch) model. Finally, this work would conclude that the Genetic-BS pricing model is exactly practical.
Keywords: genetic algorithm, Genetic-BS, option pricing model.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2089
1241 Comparative Study of Ant Colony and Genetic Algorithms for VLSI Circuit Partitioning
Authors: Sandeep Singh Gill, Rajeevan Chandel, Ashwani Chandel
This paper presents a comparative study of Ant Colony and Genetic Algorithms for VLSI circuit bi-partitioning. Ant colony optimization is an optimization method based on behaviour of social insects  whereas Genetic algorithm is an evolutionary optimization technique based on Darwinian Theory of natural evolution and its concept of survival of the fittest . Both the methods are stochastic in nature and have been successfully applied to solve many Non Polynomial hard problems. Results obtained show that Genetic algorithms out perform Ant Colony optimization technique when tested on the VLSI circuit bi-partitioning problem.
Keywords: Partitioning, genetic algorithm, ant colony optimization, non-polynomial hard, netlist, mutation.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2121
1240 Application of Genetic Algorithms for Evolution of Quantum Equivalents of Boolean Circuits
Authors: Swanti Satsangi, Ashish Gulati, Prem Kumar Kalra, C. Patvardhan
Abstract:Due to the non- intuitive nature of Quantum algorithms, it becomes difficult for a classically trained person to efficiently construct new ones. So rather than designing new algorithms manually, lately, Genetic algorithms (GA) are being implemented for this purpose. GA is a technique to automatically solve a problem using principles of Darwinian evolution. This has been implemented to explore the possibility of evolving an n-qubit circuit when the circuit matrix has been provided using a set of single, two and three qubit gates. Using a variable length population and universal stochastic selection procedure, a number of possible solution circuits, with different number of gates can be obtained for the same input matrix during different runs of GA. The given algorithm has also been successfully implemented to obtain two and three qubit Boolean circuits using Quantum gates. The results demonstrate the effectiveness of the GA procedure even when the search spaces are large.
Keywords: Ancillas, Boolean functions, Genetic algorithm, Oracles, Quantum circuits, Scratch bitProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1808
1239 Comparative study of the Genetic Algorithms and Hessians Method for Minimization of the Electric Power Production Cost
Authors: L. Abdelmalek, M. Zerikat, M. Rahli
Abstract:In this paper, we present a comparative study of the genetic algorithms and Hessian-s methods for optimal research of the active powers in an electric network of power. The objective function which is the performance index of production of electrical energy is minimized by satisfying the constraints of the equality type and inequality type initially by the Hessian-s methods and in the second time by the genetic Algorithms. The results found by the application of AG for the minimization of the electric production costs of power are very encouraging. The algorithms seem to be an effective technique to solve a great number of problems and which are in constant evolution. Nevertheless it should be specified that the traditional binary representation used for the genetic algorithms creates problems of optimization of management of the large-sized networks with high numerical precision.
Keywords: Genetic algorithm, Flow of optimum loadimpedances, Hessians method, Optimal distribution.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1189
1238 Feature Weighting and Selection - A Novel Genetic Evolutionary Approach
Authors: Serkawt Khola
Abstract: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.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1731
1237 Application of Adaptive Genetic Algorithm in Function Optimization
Authors: Panpan Xu, Shulin Sui
The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.
Keywords: Genetic algorithm, Adaptive genetic algorithm, Function optimization.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1456
1236 Evolving Neural Networks using Moment Method for Handwritten Digit Recognition
Authors: H. El Fadili, K. Zenkouar, H. Qjidaa
Abstract:This paper proposes a neural network weights and topology optimization using genetic evolution and the backpropagation training algorithm. The proposed crossover and mutation operators aims to adapt the networks architectures and weights during the evolution process. Through a specific inheritance procedure, the weights are transmitted from the parents to their offsprings, which allows re-exploitation of the already trained networks and hence the acceleration of the global convergence of the algorithm. In the preprocessing phase, a new feature extraction method is proposed based on Legendre moments with the Maximum entropy principle MEP as a selection criterion. This allows a global search space reduction in the design of the networks. The proposed method has been applied and tested on the well known MNIST database of handwritten digits.
Keywords: Genetic algorithm, Legendre Moments, MEP, Neural Network.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1557
1235 A Study of Cooperative Co-evolutionary Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem
Authors: Lee Yih Rou, Hishammuddin Asmuni
Abstract:Flexible Job Shop Problem (FJSP) is an extension of classical Job Shop Problem (JSP). The FJSP extends the routing flexibility of the JSP, i.e assigning machine to an operation. Thus it makes it more difficult than the JSP. In this study, Cooperative Coevolutionary Genetic Algorithm (CCGA) is presented to solve the FJSP. Makespan (time needed to complete all jobs) is used as the performance evaluation for CCGA. In order to test performance and efficiency of our CCGA the benchmark problems are solved. Computational result shows that the proposed CCGA is comparable with other approaches.
Keywords: Co-evolution, Genetic Algorithm (GA), Flexible JobShop Problem(FJSP)Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1663
1234 Fundamental Theory of the Evolution Force: Gene Engineering utilizing Synthetic Evolution Artificial Intelligence
Authors: L. K. Davis
Abstract:The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so we introduce the “Fundamental Theory of the Evolution Force: FTEF”. We utilized synthetic evolution artificial intelligence (SYN-AI) to identify genomic building blocks and to engineer 14-3-3 ζ docking proteins by transforming gene sequences into time-based DNA codes derived from protein hierarchical structural levels. The aforementioned served as templates for random DNA hybridizations and genetic assembly. The application of hierarchical DNA codes allowed us to fast forward evolution, while dampening the effect of point mutations. Natural selection was performed at each hierarchical structural level and mutations screened using Blosum 80 mutation frequency-based algorithms. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 ζ.
Keywords: 14-3-3 docking genes, synthetic protein design, time based DNA codes, writing DNA code from scratch.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 444
1233 Genetic Mining: Using Genetic Algorithm for Topic based on Concept Distribution
Authors: S. M. Khalessizadeh, R. Zaefarian, S.H. Nasseri, E. Ardil
Abstract:Today, Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to text classification, summarization and information retrieval system in text mining process. This researches show a better performance due to the nature of Genetic Algorithm. In this paper a new algorithm for using Genetic Algorithm in concept weighting and topic identification, based on concept standard deviation will be explored.
Keywords: Genetic Algorithm, Text Mining, Term Weighting, Concept Extraction, Concept Distribution.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3542
1232 Evaluation of Evolution Strategy, Genetic Algorithm and their Hybrid on Evolving Simulated Car Racing Controllers
Authors: Hidehiko Okada, Jumpei Tokida
Abstract:Researchers have been applying tional intelligence (AI/CI) methods to computer games. In this research field, further researchesare required to compare AI/CI methods with respect to each game application. In th our experimental result on the comparison of three evolutionary algorithms – evolution strategy, genetic algorithm, and their hybrid applied to evolving controller agents for the CIG 2007 Simulated Car Racing competition. Our experimental result shows that, premature convergence of solutions was observed in the case of ES, and GA outperformed ES in the last half of generations. Besides, a hybrid which uses GA first and ES next evolved the best solution among the whole solutions being generated. This result shows the ability of GA in globally searching promising areas in the early stage and the ability of ES in locally searching the focused area (fine-tuning solutions).
Keywords: Evolutionary algorithm, autonomous agent, neuroevolutions, simulated car racing.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1708
1231 Analog Circuit Design using Genetic Algorithm: Modified
Authors: Amod P. Vaze
Genetic Algorithm has been used to solve wide range of optimization problems. Some researches conduct on applying Genetic Algorithm to analog circuit design automation. These researches show a better performance due to the nature of Genetic Algorithm. In this paper a modified Genetic Algorithm is applied for analog circuit design automation. The modifications are made to the topology of the circuit. These modifications will lead to a more computationally efficient algorithm.
Keywords: Genetic algorithm, analog circuits, design.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2111
1230 Selective Mutation for Genetic Algorithms
Authors: Sung Hoon Jung
Abstract:In this paper, we propose a selective mutation method for improving the performances of genetic algorithms. In selective mutation, individuals are first ranked and then additionally mutated one bit in a part of their strings which is selected corresponding to their ranks. This selective mutation helps genetic algorithms to fast approach the global optimum and to quickly escape local optima. This results in increasing the performances of genetic algorithms. We measured the effects of selective mutation with four function optimization problems. It was found from extensive experiments that the selective mutation can significantly enhance the performances of genetic algorithms.
Keywords: Genetic algorithm, selective mutation, function optimizationProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1723
1229 Restartings: A Technique to Improve Classic Genetic Algorithms Performance
Authors: Grigorios N. Beligiannis, Georgios A. Tsirogiannis, Panayotis E. Pintelas
In this contribution, a way to enhance the performance of the classic Genetic Algorithm is proposed. The idea of restarting a Genetic Algorithm is applied in order to obtain better knowledge of the solution space of the problem. A new operator of 'insertion' is introduced so as to exploit (utilize) the information that has already been collected before the restarting procedure. Finally, numerical experiments comparing the performance of the classic Genetic Algorithm and the Genetic Algorithm with restartings, for some well known test functions, are given.
Keywords: Genetic Algorithms, Restartings, Search space exploration, Search space exploitation.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2020
1228 Analysis of Genotype Size for an Evolvable Hardware System
Authors: Emanuele Stomeo, Tatiana Kalganova, Cyrille Lambert
The evolution of logic circuits, which falls under the heading of evolvable hardware, is carried out by evolutionary algorithms. These algorithms are able to automatically configure reconfigurable devices. One of main difficulties in developing evolvable hardware with the ability to design functional electrical circuits is to choose the most favourable EA features such as fitness function, chromosome representations, population size, genetic operators and individual selection. Until now several researchers from the evolvable hardware community have used and tuned these parameters and various rules on how to select the value of a particular parameter have been proposed. However, to date, no one has presented a study regarding the size of the chromosome representation (circuit layout) to be used as a platform for the evolution in order to increase the evolvability, reduce the number of generations and optimize the digital logic circuits through reducing the number of logic gates. In this paper this topic has been thoroughly investigated and the optimal parameters for these EA features have been proposed. The evolution of logic circuits has been carried out by an extrinsic evolvable hardware system which uses (1+λ) evolution strategy as the core of the evolution.
Keywords: Evolvable hardware, genotype size, computational intelligence, design of logic circuits.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1528
1227 Initializing K-Means using Genetic Algorithms
Authors: Bashar Al-Shboul, Sung-Hyon Myaeng
Abstract:K-Means (KM) is considered one of the major algorithms widely used in clustering. However, it still has some problems, and one of them is in its initialization step where it is normally done randomly. Another problem for KM is that it converges to local minima. Genetic algorithms are one of the evolutionary algorithms inspired from nature and utilized in the field of clustering. In this paper, we propose two algorithms to solve the initialization problem, Genetic Algorithm Initializes KM (GAIK) and KM Initializes Genetic Algorithm (KIGA). To show the effectiveness and efficiency of our algorithms, a comparative study was done among GAIK, KIGA, Genetic-based Clustering Algorithm (GCA), and FCM .
Keywords: Clustering, Genetic Algorithms, K-means.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1925
1226 Solving the Quadratic Assignment Problems by a Genetic Algorithm with a New Replacement Strategy
Authors: Yongzhong Wu, Ping Ji
Abstract:This paper proposes a genetic algorithm based on a new replacement strategy to solve the quadratic assignment problems, which are NP-hard. The new replacement strategy aims to improve the performance of the genetic algorithm through well balancing the convergence of the searching process and the diversity of the population. In order to test the performance of the algorithm, the instances in QAPLIB, a quadratic assignment problem library, are tried and the results are compared with those reported in the literature. The performance of the genetic algorithm is promising. The significance is that this genetic algorithm is generic. It does not rely on problem-specific genetic operators, and may be easily applied to various types of combinatorial problems.
Keywords: Quadratic assignment problem, Genetic algorithm, Replacement strategy, QAPLIB.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2602
1225 Designing a Novel General Sorting Network Constructor Using Artificial Evolution
Authors: Michal Bidlo, Radek Bidlo, Lukas Sekanina
Abstract:A method is presented for the construction of arbitrary even-input sorting networks exhibiting better properties than the networks created using a conventional technique of the same type. The method was discovered by means of a genetic algorithm combined with an application-specific development. Similarly to human inventions in the area of theoretical computer science, the evolved invention was analyzed: its generality was proven and area and time complexities were determined.
Keywords: Development, genetic algorithm, program, sorting network.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1191
1224 Genetic Algorithm Based Deep Learning Parameters Tuning for Robot Object Recognition and Grasping
Authors: Delowar Hossain, Genci Capi
This paper concerns with the problem of deep learning parameters tuning using a genetic algorithm (GA) in order to improve the performance of deep learning (DL) method. We present a GA based DL method for robot object recognition and grasping. GA is used to optimize the DL parameters in learning procedure in term of the fitness function that is good enough. After finishing the evolution process, we receive the optimal number of DL parameters. To evaluate the performance of our method, we consider the object recognition and robot grasping tasks. Experimental results show that our method is efficient for robot object recognition and grasping.
Keywords: Deep learning, genetic algorithm, object recognition, robot grasping.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1961
1223 Application of Hybrid Genetic Algorithm Based on Simulated Annealing in Function Optimization
Authors: Panpan Xu, Shulin Sui, Zongjie Du
Abstract:Genetic algorithm is widely used in optimization problems for its excellent global search capabilities and highly parallel processing capabilities; but, it converges prematurely and has a poor local optimization capability in actual operation. Simulated annealing algorithm can avoid the search process falling into local optimum. A hybrid genetic algorithm based on simulated annealing is designed by combining the advantages of genetic algorithm and simulated annealing algorithm. The numerical experiment represents the hybrid genetic algorithm can be applied to solve the function optimization problems efficiently.
Keywords: Genetic algorithm, Simulated annealing, Hybrid genetic algorithm, Function optimization.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2286
1222 A Genetic Algorithm Approach for Solving Fuzzy Linear and Quadratic Equations
Authors: M. Hadi Mashinchi, M. Reza Mashinchi, Siti Mariyam H. J. Shamsuddin
In this paper a genetic algorithms approach for solving the linear and quadratic fuzzy equations Ãx̃=B̃ and Ãx̃2 + B̃x̃=C̃ , where Ã, B̃, C̃ and x̃ are fuzzy numbers is proposed by genetic algorithms. Our genetic based method initially starts with a set of random fuzzy solutions. Then in each generation of genetic algorithms, the solution candidates converge more to better fuzzy solution x̃b . In this proposed method the final reached x̃b is not only restricted to fuzzy triangular and it can be fuzzy number.
Keywords: Fuzzy coefficient, fuzzy equation, genetic algorithms.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2069
1221 Solving Bus Terminal Location Problem Using Genetic Algorithm
Authors: S. Babaie-Kafaki, R. Ghanbari, S.H. Nasseri, E. Ardil
Abstract:Bus networks design is an important problem in public transportation. The main step to this design, is determining the number of required terminals and their locations. This is an especial type of facility location problem, a large scale combinatorial optimization problem that requires a long time to be solved. The genetic algorithm (GA) is a search and optimization technique which works based on evolutionary principle of natural chromosomes. Specifically, the evolution of chromosomes due to the action of crossover, mutation and natural selection of chromosomes based on Darwin's survival-of-the-fittest principle, are all artificially simulated to constitute a robust search and optimization procedure. In this paper, we first state the problem as a mixed integer programming (MIP) problem. Then we design a new crossover and mutation for bus terminal location problem (BTLP). We tested the different parameters of genetic algorithm (for a sample problem) and obtained the optimal parameters for solving BTLP with numerical try and error.
Keywords: Bus networks, Genetic algorithm (GA), Locationproblem, Mixed integer programming (MIP).Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2183
1220 A New Algorithm to Stereo Correspondence Using Rank Transform and Morphology Based On Genetic Algorithm
Authors: Razagh Hafezi, Ahmad Keshavarz, Vida Moshfegh
Abstract:This paper presents a novel algorithm of stereo correspondence with rank transform. In this algorithm we used the genetic algorithm to achieve the accurate disparity map. Genetic algorithms are efficient search methods based on principles of population genetic, i.e. mating, chromosome crossover, gene mutation, and natural selection. Finally morphology is employed to remove the errors and discontinuities.
Keywords: genetic algorithm, morphology, rank transform, stereo correspondenceProcedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2048
1219 Statistical Genetic Algorithm
Authors: Mohammad Ali Tabarzad, Caro Lucas, Ali Hamzeh
Abstract:Adaptive Genetic Algorithms extend the Standard Gas to use dynamic procedures to apply evolutionary operators such as crossover, mutation and selection. In this paper, we try to propose a new adaptive genetic algorithm, which is based on the statistical information of the population as a guideline to tune its crossover, selection and mutation operators. This algorithms is called Statistical Genetic Algorithm and is compared with traditional GA in some benchmark problems.
Keywords: Genetic Algorithms, Statistical Information ofthe Population, PAUX, SSO.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1629
1218 The Knowledge Representation of the Genetic Regulatory Networks Based on Ontology
Authors: Ines Hamdi, Mohamed Ben Ahmed
The understanding of the system level of biological behavior and phenomenon variously needs some elements such as gene sequence, protein structure, gene functions and metabolic pathways. Challenging problems are representing, learning and reasoning about these biochemical reactions, gene and protein structure, genotype and relation between the phenotype, and expression system on those interactions. The goal of our work is to understand the behaviors of the interactions networks and to model their evolution in time and in space. We propose in this study an ontological meta-model for the knowledge representation of the genetic regulatory networks. Ontology in artificial intelligence means the fundamental categories and relations that provide a framework for knowledge models. Domain ontology's are now commonly used to enable heterogeneous information resources, such as knowledge-based systems, to communicate with each other. The interest of our model is to represent the spatial, temporal and spatio-temporal knowledge. We validated our propositions in the genetic regulatory network of the Aarbidosis thaliana flower
Keywords: Ontological model, spatio-temporal modeling, Genetic Regulatory Networks (GRNs), knowledge representation.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1376
1217 An Innovative Fuzzy Decision Making Based Genetic Algorithm
Authors: M. A. Sharbafi, M. Shakiba Herfeh, Caro Lucas, A. Mohammadi Nejad
Abstract:Several researchers have proposed methods about combination of Genetic Algorithm (GA) and Fuzzy Logic (the use of GA to obtain fuzzy rules and application of fuzzy logic in optimization of GA). In this paper, we suggest a new method in which fuzzy decision making is used to improve the performance of genetic algorithm. In the suggested method, we determine the alleles that enhance the fitness of chromosomes and try to insert them to the next generation. In this algorithm we try to present an innovative vaccination in the process of reproduction in genetic algorithm, with considering the trade off between exploration and exploitation.
Keywords: Genetic Algorithm, Fuzzy Decision Making.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1436