free research papers-computer science-genetic algorithm
Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
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ABSTRACT A novel and robust automated docking method that predicts the bound conformations of flexible ligands to macromolecular targets has been developed and tested, in combination with a new scoring function that estimates the free energy change upon
A genetic algorithm tutorial
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This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical
Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion
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GARLI (Genetic Algorithm for Rapid Likelihood Inference) is a heuristic search algorithm for performing phylogenetic inference under the maximum likelihood (ML) criterion. The algorithm was developed with the goals of increasing both the speed of ML inference and
A genetic algorithm for 3D protein folding simulations
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ABSTRACT In this report we present a genetic algorithm that is suitable for 3D protein folding simulations. Some biological background on protein structure and protein folding is given. The current computational approaches to the protein folding problem are outlined. Then
Conventional genetic algorithm for job shop problems
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Abstract The job shop problem (JSP) is NP-hard, much harder than the traveling salesman problem. This paper shows how a conventional Genetic Algorithm (GA) can efficiently solve the JSP. We introduce unique ideas in representation, evaluation, and survival. A solution
A genetic algorithm applicable to large-scale job-shop problems
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Abstract This paper proposes a novel method for solving job-shop problems based on Genetic Algorithms (GAs). In this method, each individual represents a solution which is derived directly from operation completion times rather than being coded in a binary string
An improved genetic algorithm for pipe network optimization
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ABSTRACT. An improved genetic algorithm (GA) formulation for pipe network optimization has been developed. The new GA uses variable power scaling of the fitness function. The exponent introduced into the fitness function is increased in magnitude as the GA
When will a genetic algorithm outperform hill climbing?
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Abstract In this paper we review some previously published experimental results in which a simple hillclimbing algorithm-Random Mutation Hill-Climbing (RMHC)-significantly outperforms a genetic algorithm on a simple" Royal Road" function. vVe present an
Artificial-intelligence-based electrical machines and drives: application of fuzzy, neural, fuzzy-neural, and genetic-algorithm-based techniques
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Author: Peter Vas, Publisher: Oxford University Press, Usa Pages: 656 Published: 1999-04-08 ISBN-10: 019859397X ISBN-13: 9780198593973 Category: computer mathematics, artificial intelligence, computer science, computersinternet, Binding: Hardcover Edition: Rating:
GAlib: A C++ library of genetic algorithm components
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GAlib is a C++ library of genetic algorithm objects. The library includes tools for using genetic algorithms to do optimization in any C++ program using any representation and any genetic operators. This documentation includes an extensive overview of how to
A hybrid grouping genetic algorithm for bin packing
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The grouping genetic algorithm (GGA) is a genetic algorithm heavily modified to suit the structure of grouping problems. Those are the problems where the aim is to find a good partition of a set or to group together the members of the set. The bin packing problem (
A micro-genetic algorithm for multiobjective optimization
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In this paper, we propose a multiobjective optimization approach based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population (four individuals were used in our experiment) and a reinitialization process. We use three
A genetic algorithm for flexible molecular overlay and pharmacophore elucidation
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A genetic algorithm (GA) has been developed for the superimposition of sets of flexible molecules. Molecules are represented by a chromosome that encodes angles of rotation about flexible bonds and mappings between hydrogen-bond donor proton, acceptor lone
A promising genetic algorithm approach to job-shop scheduling, rescheduling, and open-shop scheduling problems
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Abstract The general job-shop scheduling problem is known to be extremely hard. We describe a GA approach which produces reasonably good results very quickly on standard benchmark job-shop scheduling problems, better than previous e orts using genetic
A comparison of particle swarm optimization and the genetic algorithm
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Introduction article Swarm Optimization (PSO) was invented by Kennedy and Eberhart1 in the mid 1990s while attempting to simulate the choreographed, graceful motion of swarms of birds as part of a sociocognitive study investigating the notion of collective intelligence in
Nonstationary function optimization using the structured genetic algorithm
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ABSTRACT In this paper, we describe the application of a new type of genetic algorithm called the Structured Genetic Algorithm (sGA) for function optimization in nonstationary environments. The novelty of this genetic model lies primarily in its redundant genetic
A genetic algorithm for the weight setting problem in OSPF routing
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With the growth of the Internet, Internet Service Providers (ISPs) try to meet the increasing traffic demand with new technology and improved utilization of existing resources. Routing of data packets can affect network utilization. Packets are sent along network paths from
A knowledge-intensive genetic algorithm for supervised learning
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Supervised learning in attribute-based spaces is one of the most popular machine learning problems studied and, consequently, has attracted considerable attention of the genetic algorithm community. The fullmemory approach developed here uses the same high-level
A parallel genetic algorithm for solving the school timetabling problem
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ABSTRACT Genetic algorithms (GA) have been applied to a number of optimisation problems with some success The algorithms mimic the process of natural selection, with the effect of creating a number of potentially optimal solutions to some complex search
A genetic algorithm for function optimization: a Matlab implementation
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A genetic algorithm implemented in Matlab is presented. Matlab is used for the following reasons: it provides many built in auxiliary functions useful for function optimization it is completely portable and it is efficient for numerical computations. The genetic algorithm
What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation
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What makes a problem easy or hard for a genetic algorithm (GA)? This question has become increasingly important as people have tried to apply the GA to ever more diverse types of problems. Much previous work on this question has studied the relationship
Convergence models of genetic algorithm selection schemes
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We discuss the use of normal distribution theory as a tool to model the convergence characteristics of different GA selection schemes. The models predict the proportion of optimal alleles in function of the number of generations when optimizing the bit-counting
An efficient genetic algorithm for job shop scheduling problems
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ABSTRACT This paper presents an efficient genetic algorithm for solving job shop scheduling problems. A schedule is encoded by a list of job sequences on machines. This paper introduces a characteristics preserving crossover named the subsequence
A hybrid genetic algorithm for classification
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ABSTRACT In this paper we describe a method for hybridizing a genetic algorithm and ak nearest neighbors classification algorithm. We use the genetic algorithm and a training data set to learn real-valued weights associated with individual attributes in the data set. We
Project scheduling with multiple modes: a genetic algorithm
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In this paper we consider the resource-constrained project scheduling problem with multiple execution modes for each activity and makespan minimization as objective. We present a new genetic algorithm approach to solve this problem. The genetic encoding is based on
A genetic algorithm based university timetabling system
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Abstract The annual construction of an Exam Timetable is a common problem for all institutions of higher education. Quite often it is done by hand or with the limited help of a simple administration system and usually involves taking the previous year's timetable
Users guide to the PGAPack parallel genetic algorithm library
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This work was supported in part by the Mathematical, Information, and Computational Sciences Division subprogram of the O ce of Computational and Technology Research, US Department of Energy, under Contract W-31-109-Eng-38. 2.6 Mailing Lists, Web Page, and Bug
A dual genetic algorithm for bounded integer programs
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1 Introduction The bounded variable integer program can be stated mathematically: min ex (
A hybrid genetic algorithm for highly constrained timetabling problems
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Abstract In this paper, we present a powerful hybrid genetic algorithm based around a heuristic timetabling framework. This combines a direct representation of the timetable with heuristic crossover operators to ensure that the most fundamental constraints are never
Using genetic algorithm for network intrusion detection
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ABSTRACT This paper describes a technique of applying Genetic Algorithm (GA) to network Intrusion Detection Systems (IDSs). A brief overview of the Intrusion Detection System, genetic algorithm, and related detection techniques is presented. Parameters and
A genetic algorithm discovers particle-based computation in cellular automata
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How does evolution produce sophisticated emergent computation in systems composed of simple components limited to local interactions? To model such a process, we used a genetic algorithm (GA) to evolve cellular automata to perform a computational task
A genetic algorithm for database query optimization
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Abstract Current query optimization techniques are inadequate to support some of the emerging database applications. In this paper, we outline a database query optimization problem and describe the adaptation of a genetic algorithm to the problem. We present a
Gassata, a genetic algorithm as an alternative tool for security audit trails analysis
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GasSAtA, a Genetic Algorithm as an Alternative Tool for Security Audit Trails Analysis Ludovic Abstract Security audit efficiency is low because the security officer has to man- age such a huge
Designing a competent simple genetic algorithm for search and optimization
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Abstract. Simple genetic algorithms have been used to solve many water resources problems, but specifying the parameters that control how adaptive search is performed can be a difficult and time-consuming trial-and-error process. However, theoretical
Optimal design of aquifer cleanup systems under uncertainty using a neural network and agenetic algorithm
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ABSTRACT. We present a methodology to account for the stochastic nature of hydraulic conductivity during the design of pump-and-treat systems for aquifer cleanup. The methodology (1) uses a genetic algorithm to find the global optimal solution and (2)
Forking genetic algorithm with blocking and shrinking modes (fGA)
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Abstract In this paper, we propose a new type of multi-population GA, that is, the forking Genetic Algorithm (fGA). The fGA is designed to solve multi-modal problems which are difficult to solve by the traditional GAs because of the many local optimums. The fGA has
Hybrid genetic algorithm, simulated annealing and tabu search methods for vehicle routing problems with time windows
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Abstract The Vehicle Routing Problem with Time Windows (VRPTW) involves servicing a set of customers, with earliest and latest time deadlines, with varying demands using capacitated vehicles with limited travel times. The objective of the problem is to service all
A parallel genetic algorithm for the set partitioning problem
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ABSTRACT In this dissertation we report on our e orts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problem| a di cult combinatorial optimization problem used by many airlines as a mathematical model for ight crew
The island model genetic algorithm: On separability, population size and convergence
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Abstract Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple
Predicting conserved water-mediated and polar ligand interactions in proteins using a K-nearest-neighbors genetic algorithm
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It is increasingly recognized that water plays an important role in protein structure and function, yet the prediction of conserved protein-water interactions has remained elusive. In addition to bulk solvent, which is crucial to the hydrophobic effect and protein folding (
A universal eclectic genetic algorithm for constrained optimization
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ABSTRACT. Several non-traditional methods of solving nonlinear constrained optimization problems have been introduced in the past: evolutionary programming, genetic algorithms and simulated annealing, among others. However these methods present some
A genetic algorithm solving a weekly course-timetabling problem
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In this paper we describe a heavily constrained university timetabling problem, and our genetic algorithm based approach to solve it. A problem-specific chromosome representation and knowledge-augmented genetic operators have been developed; these
Statistical dynamics of the royal road genetic algorithm
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Abstract. Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alternate between periods of stasis and brief periods of rapid change in their behavior. In this paper an analytical model for the dynamics of a mutationonly genetic
Genetic algorithm inversion for receiver functions with application to crust and uppermost mantle structure beneath eastern Australia
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Abstract. Genetic algorithm (GA) inversion, a nonlinear global optimization technique, has been applied to determine crustal and uppermost mantle velocity structure from teleseismic receiver functions. With a new modelling in which not only the 5" wave velocity but also
A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces
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Abstract Differential Evolution (DE) is a simple genetic algorithm for numerical optimization in real parameter spaces. In a statistical context one would not just want the optimum but also its uncertainty. The uncertainty distribution can be obtained by a Bayesian analysis (
A hardware genetic algorithm for the traveling salesman problem on Splash 2
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With the introduction of Splash, Splash 2, PAM, and other reconfigurable computers, a wide variety of algorithms can now be feasibly constructed in hardware. In this paper, we describe the Splash 2 Parallel Genetic Algorithm (SPGA), which is a parallel genetic algorithm for
A genetic algorithm for structure-based de novo design
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Genetic algorithms have properties which make them attractive in de novo drug design. Like other de novo design programs, genetic algorithms require a method to reduce the enormous search space of possible compounds. Most often this is done using information
A genetic algorithm methodology for complex scheduling problems
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ABSTRACT This paper considers the scheduling problem to minimize total tardiness given multiple machines, ready times, sequence dependent setups, machine downtime and scarce tools. We develop a genetic algorithm based on random keys representation, elitist
GAMBL, genetic algorithm optimization of memory-based WSD
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Abstract GAMBL is a word expert approach to WSD in which each word expert is trained using memorybased learning. Joint feature selection and algorithm parameter optimization are achieved with a genetic algorithm (GA). We use a cascaded classifier approach in
Traffic signal optimization program for oversaturated conditions: Genetic algorithm approach
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Traffic signal optimization programs have been used widely among transportation professionals. However, none of the existing computer programs can optimize all four traffic control parameters (ie, cycle length, green split, offset, and phase sequence)
Real-coded genetic algorithm for rule-based flood control reservoir management
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Genetic algorithms (GAs) have been fairly successful in a diverse range of optimization problems, providing an efficient and robust way for guiding a search even in a complex system and in the absence of domain knowledge. In this paper, two types of genetic
A genetic algorithm approach to dynamic job shop scheduling problems
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ABSTRACT This paper describes a genetic algorithm approach to the dynamic job shop scheduling problem with jobs arriving continually. Both deterministic and stochastic models of the dynamic problem were investigated. The objective functions examined were
NCGA: Neighborhood cultivation genetic algorithm for multi-objective optimization problems
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ABSTRACT In this paper, a new genetic algorithm for multiobjective optimization problems is introduced. That is called Neighborhood Cultivation GA (NCGA). In the recent studies such as SPEA2 or NSGA-II, it is demonstrated that some mechanisms are important; the
Sensor network optimization using a genetic algorithm
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ABSTRACT In this paper, we propose an efficient method based on genetic algorithms (GAs) to solve a sensor network optimization problem. Long communication distances between sensors and a sink (or destination) in a sensor network can greatly drain the
A genetic algorithm for resource-constrained scheduling
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ABSTRACT This work describes a genetic algorithm approach to resource-constrained scheduling using a direct, time-based representation. Whereas traditional solution methods are typically sequencebased, this representation encodes schedule information as a dual
The shifting balance genetic algorithm: Improving the GA in a dynamic environment
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ABSTRACT Two observed deficiencies of the GA are its tendency to get trapped at local maxima and the difficulty it has handling a changing environment after convergence has occurred. A mechanism proposed by Sewall Wright in the 1930s addresses the problem of
Towards creative evolutionary systems with interactive genetic algorithm
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Evolutionary computation has shown a great potential to work out several real-world problems in the point of optimization, but it is still quite far from realizing a system of matching the human performance. Especially, in creative applications such as architecture
The microbial genetic algorithm
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We analyse how the conventional Genetic Algorithm can be stripped down and reduced to its basics. We present a minimal, modified version that can be interpreted in terms of horizontal gene transfer, as in bacterial conjugation. Whilst its functionality is effectively
Enhanced genetic algorithm for signal-timing optimization of oversaturated intersections
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Enhancements were provided to a previously developed genetic algorithm (GA) for traffic signal optimization for oversaturated traffic conditions. A broader range of optimization strategies was provided to include modified delay minimization with a penalty function and
Genetic algorithm based schedulers for grid computing systems
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ABSTRACT. In this paper we present Genetic Algorithms (GAs) based schedulers for efficiently allocating jobs to resources in a Grid system. Scheduling is a key problem in emergent computational systems, such as Grid and P2P, in order to benefit from the large computing
A grouping genetic algorithm for graph colouring and exam timetabling
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It has frequently been reported that pure genetic algorithms for graph colouring are in general outperformed by more conventional methods. There is every reason to believe that this is mainly due to the choice of an unsuitable encoding of solutions. Therefore, an
A multi-objective genetic algorithm for solving assembly line balancing problem
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In this paper, a multi-objective genetic agorithm to solve assembly line balancing problems is proposed. The performance criteria considered are the number of workstations, the line efficiency, the smoothness index before trade and transfer, and the smoothness index
A genetic algorithm for multi-level, multi-machine lot sizing and scheduling
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ABSTRACT This contribution introduces a mixed integer programming formulation for the multi level, multi machine proportional lot sizing and scheduling problem. It also presents a genetic algorithm to solve that problem. The e ciency of that algorithm is due to an
A hybrid genetic algorithm for the capacitated vehicle routing problem
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Recently proved successful for variants of the vehicle routing problem (VRP) involving time windows, genetic algorithms have not yet shown to compete or challenge current best search techniques in solving the classical capacitated VRP. In this paper, a hybrid genetic
A fuzzy genetic algorithm approach to an adaptive information retrieval agent
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We present an approach to a Genetic Information Retrieval Agent Filter (GIRAF) for documents from the Internet using a genetic algorithm (GA) with fuzzy set genes to learn the user's information needs. The population of chromosomes with fixed length represents
Protein folding simulation with genetic algorithm and supersecondary structure constraints
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ABSTRACT We describe an algorithm to compute native structures of proteins from their primary sequences. The novel aspects of this method are: 1) The hydrophobic potential was set to be proportional to the nonpolar solvent accessible surface. To make computation
Color image segmentation with genetic algorithm for in-field weed sensing
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Abstract. This study was undertaken to develop machine vision-based weed detection technology for outdoor natural lighting conditions. Supervised color image segmentation using a binary-coded genetic algorithm (GA) identifying a region in Hue-Saturation-
Aerodynamic and aeroacoustic optimization of rotocraft airfoils via a parallel genetic algorithm
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THIS paper discusses the application of a parallel genetic algorithm (GA) to a multiobjective airfoil design problem. Airfoil shapes were designed to minimize the drag coeffi cient and the overallaveragedsound-pressurelevel (OASPL) forthree owconditions representative of
Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos
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Abstract Genetic algorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature
A genetic algorithm with variable range of local search for tracking changing environments
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In this paper we examine a modification to the genetic algorithm-a new adaptive operator was developed for two industrial applications using genetic algorithm based on-line control systems. The aim is to enable the control systems to track optima of a time-varying
Reconditioning inverse problems using the genetic algorithm and revised parameterization
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Abstract The better conditioned an inverse problem is, the more independent pieces of information may be transferred from the data to the model solution, and the less independent prior information must be added to resolve trade offs. We present a practical measure of
Comparison of genetic algorithm based prototype selection schemes
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Prototype selection is the process of" nding representative patterns from the data. Representative patterns help in reducing the data on which further operations such as data mining can be carried out. The current work discusses computation of prototypes using
Sensor placement for on-orbit modal identification via a genetic algorithm
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A variant of the genetic algorithm is used to place sensors optimally on a large space structure for the purpose of modal identification. The selection and reproduction schemes of the genetic algorithm are modified, and a new operator called forced mutation is
Implementation of standard genetic algorithm on MIMD machines
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Genetic Algorithms (GAs) have been implemented on a number of multiprocessor machines. In many cases the GA has been adapted to the hardware structure of the system. This paper describes the implementation of a standard genetic algorithm on several MIMD
Genetic algorithm optimization of membership functions for mining fuzzy association rules
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A large number of papers have been published regarding the combination of fuzzy logic (FL) and genetic algorithms (GA)(Herrera and Verdegay 1999). Fuzzy logic is a useful tool for modeling complex systems and deriving useful fuzzy relations or rules (Okamura et. al.
A two-dimensional genetic algorithm for the Ising problem
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ABSTRACT. The genetic algorithm is a powerful heuristic for the solution of hard combinatorial problems and has been investigated by numerous authors. Many problems, arising for example in communi-. cation networks, possess strong two-dimensional characteristics.
A genetic algorithm-based approach to data mining
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ABSTRACT Most data mining systems to date have used variants of traditional machine- learning algorithms to tackle the task of directed knowledge discovery. This paper presents an approach which, as well as being useful for such directed data mining, can also be
Iterative fixture layout and clamping force optimization using the genetic algorithm
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Fixtures are used to locate, support and completely constrain the workpiece during machining. The design of a fixture that permits accurate machining of the workpiece by keeping the contribution of workpiece/fixture elastic deformation to the machining error
On economic applications of the genetic algorithm: a model of the cobweb type
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Abstract. This paper explores the idea of using artificial adaptive agents in economic theory. In particular, we use Genetic Algorithms (GAs) to model the learning behavior of a population of adaptive and boundedly rational agents interacting in an economic system.
Hybrid approach for optimal nesting using a genetic algorithm and a local minimization algorithm
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ABSTRACT In layout design problems including blank nesting, the positions and directions of layout elements must be determined so as to minimize the total space. It is difficult and computationally time-consuming to find the optimal solution for such layout problems,
A genetic algorithm for the generation of jazz melodies
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Abstract This paper describes a system for the generation of jazz melodies over an input chord progression. A genetic algorithm was used to search through the space of possible solutions. A symbolic, as opposed to binary, approach with domain-specific reproduction
Synthesis of compliant mechanisms for path generation using genetic algorithm
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In this paper is described a procedure to synthesize the optimal topology, shape, and size of compliant continua for a given nonlinear output path. The path is prescribed using a finite number of distinct precision points much in accordance with the synthesis for path
A genetic algorithm for generalized rule induction
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knowledge discovery, generalized rule induction.
GA-EDA: Hybrid evolutionary algorithm using genetic and estimation of distribution algorithms
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Examples of them are: ERA, which incorporates, simulated annealing. [Rodriguez-Tello
A genetic algorithm based approach to optimal toll level and location problems
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The derivative based approach to solve the optimal toll problem is demonstrated in this paper for a medium scale network. It is shown that although the method works for most small problems with only a few links tolled, it fails to converge for larger scale problems. This
Optimal tolerance allocation based on fuzzy comprehensive evaluation and genetic algorithm
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It is very important to know how to allocate tolerances economically for parts in a CAD/CAM system because this directly affects the machining costs of the parts. A new approach based on fuzzy comprehensive evaluation (FCE) and a genetic algorithm (GA) is presented to
A hybrid genetic algorithm for the vehicle routing problem with time windows
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A variety of hybrid genetic algorithms has been recently proposed to address the vehicle routing problem with time windows (VRPTW), a problem known to be NP-hard. However, very few genetic-based approaches exploit implicit knowledge provided by the structure of
Structural application of a shape optimization method based on a genetic algorithm
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ABSTRACT. This paper presents a structural application of a shape optimization method based on a Genetic Algorithm (GA). The method produces a sequence of fixed-distance step-wise movements of the boundary nodes of a finite element model to derive optimal shapes from
A hybrid decision tree/genetic algorithm for coping with the problem of small disjuncts in data mining
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Abstract The problem of small disjuncts is a serious challenge for data mining algorithms. In essence, small disjuncts are rules covering a small number of examples. Due to their nature, small disjuncts tend to be error prone and contribute to a decrease in predictive accuracy.
A practical schema theorem for genetic algorithm design and tuning
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Abstract This paper develops the theory that can enable the design of genetic algorithms and choose the parameters such that the proportion of the best building blocks grow. A practical schema theorem has been used for this purpose and its ramification for the
A parallel genetic algorithm for multi-objective microprocessor design
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Abstract The microprocessor chip designer must solve the problem of partitioning millions of transistors into an arbitrary number of hardware structures within a ?nite chip area toward achieving maximum performance. This combinative complexity is compounded by a
Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision
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ABSTRACT Genetic algorithms (GAs) are efficient search methods based on the paradigm of natural selection and population genetics. A simple GA was applied for selecting the optimal feature subset among an initial feature set of larger size. The performances were tested on
A fuzzy genetic algorithm for real-world job shop scheduling
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In this paper, a multi-objective genetic algorithm is proposed to deal with a real-world fuzzy job shop scheduling problem. Fuzzy sets are used to model uncertain due dates and processing times of jobs. The objectives considered are average tardiness and the
Case History Some practical aspects of prestack waveform inversion using a genetic algorithm: An example from the east Texas Woodbine gas sand
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ABSTRACT In this paper, a prestack inversion method using a genetic algorithm (GA) is presented, and issues relating to the implementation of prestack GA inversion in practice are discussed. GA is a Monte-Carlo type inversion, using a natural analogy to the biological
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