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  • Articles  (32)
  • genetic algorithms  (32)
  • Springer  (32)
  • American Chemical Society
  • 1995-1999  (32)
  • Computer Science  (32)
  • Natural Sciences in General
  • 1
    Electronic Resource
    Electronic Resource
    Fuzzy-enhanced Adaptive Control for Flexible Drive System with Friction Using Genetic Algorithms (1998)
    Springer
    Journal of intelligent and robotic systems 23 (1998), S. 379-405 
    Details
    ISSN: 1573-0409
    Keywords: flexible drive system ; fuzzy-enhanced adaptive control ; genetic algorithms ; friction control
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract When a mechatronic system is in slow speed motion, serious effect of nonlinear friction plays a key role in its control design. In this paper, a stable adaptive control for drive systems including transmission flexibility and friction, based on the Lyapunov stability theory, is first proposed. For ease of design, the friction is fictitiously assumed as an unknown disturbance in the derivation of the adaptive control law. Genetic algorithms are then suggested for learning the structure and parameters of the fuzzy-enhancing strategy for the adaptive control to improve system's transient performance and robustness with respect to uncertainty. The integrated fuzzy-enhanced adaptive control is well tested via computer simulations using the new complete dynamic friction model recently suggested by Canudas de Wit et al. for modeling the real friction phenomena. Much lower critical velocity of a flexible drive system that determines system's low-speed performance bound can be obtained using the proposed hybrid control strategy.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008073402905
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  • 2
    Electronic Resource
    Electronic Resource
    Contact Friction Compensation for Robots Using Genetic Learning Algorithms (1998)
    Springer
    Journal of intelligent and robotic systems 23 (1998), S. 331-349 
    Details
    ISSN: 1573-0409
    Keywords: constrained robots ; genetic algorithms ; learning ; friction compensation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract In this paper, the issues of contact friction compensation for constrained robots are presented. The proposed design consists of two loops. The inner loop is for the inverse dynamics control which linearizes the system by canceling nonlinear dynamics, while the outer loop is for friction compensation. Although various models of friction have been proposed in many engineering applications, frictional force can be modeled by the Coulomb friction plus the viscous force. Based on such a model, an on-line genetic algorithm is proposed to learn the friction coefficients for friction model. The friction compensation control input is also implemented in terms of the friction coefficients to cancel the effect of unknown friction. By the guidance of the fitness function, the genetic learning algorithm searches for the best-fit value in a way like the natural surviving laws. Simulation results demonstrate that the proposed on-line genetic algorithm can achieve good friction compensation even under the conditions of measurement noise and system uncertainty. Moreover, the proposed control scheme is also found to be feasible for friction compensation of friction model with Stribeck effect and position-dependent friction model.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008044317911
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  • 3
    Electronic Resource
    Electronic Resource
    Collision-Free Cartesian Trajectory Generation Using Raster Scanning and Genetic Algorithms (1998)
    Springer
    Journal of intelligent and robotic systems 23 (1998), S. 351-377 
    Details
    ISSN: 1573-0409
    Keywords: robots ; trajectory generation ; raster scanning ; genetic algorithms ; redundancy ; obstacle avoidance
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract An algorithm for Cartesian trajectory generation by redundant robots in environments with obstacles is presented. The algorithm combines a raster scanning technique, genetic algorithms and functions for interpolation in the joint coordinates space in order to approximate a desired Cartesian curve by the robot's hand tip under maximum allowed position deviation. A raster scanning technique determines a minimal set of knot points on the desired curve in order to generate a Cartesian trajectory with bounded position approximation error. Genetic algorithms are used to determine an acceptable robot configuration under obstacle avoidance constraints corresponding to a knot point. Robot motion between two successive knot points is finally achieved using well known interpolation techniques in the joint coordinates space. The proposed algorithm is analyzed and its performance is demonstrated through simulated experiments carried out on planar redundant robots.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008001930450
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  • 4
    Electronic Resource
    Electronic Resource
    Intelligent Control for an Acrobot (1997)
    Springer
    Journal of intelligent and robotic systems 18 (1997), S. 209-248 
    Details
    ISSN: 1573-0409
    Keywords: acrobot ; robotics ; fuzzy control ; genetic algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The acrobot is an underactuated two-link planar robot that mimics the human acrobat who hangs from a bar and tries to swing up to a perfectly balanced upside-down position with his/her hands still on the bar. In this paper we develop intelligent controllers for swing-up and balancing of the acrobot. In particular, we first develop classical, fuzzy, and adaptive fuzzy controllers to balance the acrobot in its inverted unstable equilibrium region. Next, a proportional-derivative (PD) controller with inner-loop partial feedback linearization, a state-feedback, and a fuzzy controller are developed to swing up the acrobot from its stable equilibrium position to the inverted region, where we use a balancing controller to ‘catch’ and balance it. At the same time, we develop two genetic algorithms for tuning the balancing and swing-up controllers, and show how these can be used to help optimize the performance of the controllers. Overall, this paper provides (i) a case study of the development of a variety of intelligent controllers for a challenging application, (ii) a comparative analysis of intelligent vs. conventional control methods (including the linear quadratic regulator and feedback linearization) for this application, and (iii) a case study of the development of genetic algorithms for off-line computer-aided-design of both conventional and intelligent control systems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007953809856
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  • 5
    Electronic Resource
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    Cognitive Architecture for Robust Adaptive Control of Robots in a Team (1997)
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 251-273 
    Details
    ISSN: 1573-0409
    Keywords: robot control ; adaptive behavior ; robust intelligent control ; multi-robot systems ; machine learning ; neural networks ; genetic algorithms ; cognitive architecture.
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract The objective of this paper is to present a cognitive architecture thatutilizes three different methodologies for adaptive, robust control ofrobots behaving intelligently in a team. The robots interact within a worldof objects, and obstacles, performing tasks robustly, while improving theirperformance through learning. The adaptive control of the robots has beenachieved by a novel control system. The Tropism-based cognitive architecturefor the individual behavior of robots in a colony is demonstrated throughexperimental investigation of the robot colony. This architecture is basedon representation of the likes and dislikes of the robots. It is shown thatthe novel architecture is not only robust, but also provides the robots withintelligent adaptive behavior. This objective is achieved by utilization ofthree different techniques of neural networks, machine learning, and geneticalgorithms. Each of these methodologies are applied to the tropismarchitecture, resulting in improvements in the task performance of the robotteam, demonstrating the adaptability and robustness of the proposed controlsystem.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007960328527
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  • 6
    Electronic Resource
    Electronic Resource
    Hierarchical Fuzzy Control for C-Axis of CNC Turning Centers Using Genetic Algorithms (1999)
    Springer
    Journal of intelligent and robotic systems 25 (1999), S. 255-275 
    Details
    ISSN: 1573-0409
    Keywords: hierarchical fuzzy control ; genetic algorithms ; flexible C-axis ; dynamic friction ; low-speed control
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract A combined PD and hierarchical fuzzy control is proposed for the low-speed control of the C-axis of CNC turning centers considering the effects of transmission flexibility and complex nonlinear friction. Learning of the hierarchical structure and parameters of the suggested control strategy is carried out by using the genetic algorithms. The proposed algorithm consists of two phases: the first one is to search the best hierarchy, and the second to tune the consequent center values of the constituent fuzzy logic systems into the hierarchy. For the least total control rule number, the hierarchical fuzzy controller is chosen to include only the simple two-input/one-output fuzzy systems, and both binary and decimal genes are used for the selection, crossover and mutation of the genetic algorithm. The proposed approach is validated by the computer simulation. Each generation consists of 30 individuals: ten reproduced from its parent generation, ten generated by crossover, and the other ten by mutation. In the simulations, the C-axis is assumed to be driven by a vector-controlled AC induction motor, and the dynamic friction model suggested by Canudas de Wit et al. in 1995 is used.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008035612395
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  • 7
    Electronic Resource
    Electronic Resource
    Genetic Algorithms, Operators, and DNA Fragment Assembly (1995)
    Springer
    Machine learning 21 (1995), S. 11-33 
    Details
    ISSN: 0885-6125
    Keywords: genetic algorithms ; DNA fragment assembly ; human genome project ; ordering problems ; edge-recombination crossover ; building blocks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We study different genetic algorithm operators for one permutation problem associated with the Human Genome Project—the assembly of DNA sequence fragments from a parent clone whose sequence is unknown into a consensus sequence corresponding to the parent sequence. The sorted-order representation, which does not require specialized operators, is compared with a more traditional permutation representation, which does require specialized operators. The two representations and their associated operators are compared on problems ranging from 2K to 34K base pairs (KB). Edge-recombination crossover used in conjunction with several specialized operators is found to perform best in these experiments; these operators solved a 10KB sequence, consisting of 177 fragments, with no manual intervention. Natural building blocks in the problem are exploited at progressively higher levels through “macro-operators.” This significantly improves performance.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022613513712
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  • 8
    Electronic Resource
    Electronic Resource
    Genetic algorithms, operators, and DNA fragment assembly (1995)
    Springer
    Machine learning 21 (1995), S. 11-33 
    Details
    ISSN: 0885-6125
    Keywords: genetic algorithms ; DNA fragment assembly ; human genome project ; ordering problems ; edgerecombination crossover ; building blocks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We study different genetic algorithm operators for one permutation problem associated with the Human Genome Project—the assembly of DNA sequence fragments from a parent clone whose sequence is unknown into a consensus sequence corresponding to the parent sequence. The sorted-order representation, which does not require specialized operators, is compared with a more traditional permutation representation, which does require specialized operators. The two representations and their associated operators are compared on problems ranging from 2K to 34K base pairs (KB). Edge-recombination crossover used in conjunction with several specialized operators is found to perform best in these experiments; these operators solved a 10KB sequence, consisting of 177 fragments, with no manual intervention. Natural building blocks in the problem are exploited at progressively higher levels through “macro-operators.” This significantly improves performance.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00993377
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  • 9
    Electronic Resource
    Electronic Resource
    Alecsys and the AutonoMouse: Learning to control a real robot by distributed classifier systems (1995)
    Springer
    Machine learning 19 (1995), S. 209-240 
    Details
    ISSN: 0885-6125
    Keywords: learning classifier systems ; reinforcement learning ; genetic algorithms ; animat problem
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this article we investigate the feasibility of using learning classifier systems as a tool for building adaptive control systems for real robots. Their use on real robots imposes efficiency constraints which are addressed by three main tools: parallelism, distributed architecture, and training. Parallelism is useful to speed up computation and to increase the flexibility of the learning system design. Distributed architecture helps in making it possible to decompose the overall task into a set of simpler learning tasks. Finally, training provides guidance to the system while learning, shortening the number of cycles required to learn. These tools and the issues they raise are first studied in simulation, and then the experience gained with simulations is used to implement the learning system on the real robot. Results have shown that with this approach it is possible to let the AutonoMouse, a small real robot, learn to approach a light source under a number of different noise and lesion conditions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00996270
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  • 10
    Electronic Resource
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    ALECSYS and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems (1995)
    Springer
    Machine learning 19 (1995), S. 209-240 
    Details
    ISSN: 0885-6125
    Keywords: learning classifier systems ; reinforcement learning ; genetic algorithms ; animat problem
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this article we investigate the feasibility of using learning classifier systems as a tool for building adaptive control systems for real robots. Their use on real robots imposes efficiency constraints which are addressed by three main tools: parallelism, distributed architecture, and training. Parallelism is useful to speed up computation and to increase the flexibility of the learning system design. Distributed architecture helps in making it possible to decompose the overall task into a set of simpler learning tasks. Finally, training provides guidance to the system while learning, shortening the number of cycles required to learn. These tools and the issues they raise are first studied in simulation, and then the experience gained with simulations is used to implement the learning system on the real robot. Results have shown that with this approach it is possible to let the AutonoMouse, a small real robot, learn to approach a light source under a number of different noise and lesion conditions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1022649410928
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  • 11
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    Fuzzy evolutionary computing (1998)
    Springer
    Soft computing 2 (1998), S. 61-72 
    Details
    ISSN: 1433-7479
    Keywords: Keywords fuzzy sets ; genetic algorithms ; information granularity ; weak encoding ; fuzzy metarules
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract  This paper elaborates on a new paradigm of computing embracing fuzzy sets and evolutionary methods (specially genetic algorithms). We discuss conceptual and algorithmic enhancements to the individual methods. Fuzzy sets are geared toward granular information processing. Evolutionary computing are population-based optimization methods. In this way, as being components of any hybrid structure, they naturally complement each other. The study reveals a number of representative symbiotic links between fuzzy and genetic computing and provides with relevant illustrative examples.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s005000050035
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  • 12
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    Artificial evolution and real robots (1997)
    Springer
    Artificial life and robotics 1 (1997), S. 35-38 
    Details
    ISSN: 1614-7456
    Keywords: artificial evolution ; genetic algorithms ; evolutionary robotics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Artificial evolution as a design methodology allows the relaxation of many of the constraints that have held back conventional methods. It does not require a complete prior analysis and decomposition of the task to be tackled, as human designers require. However, this freedom comes at some cost; there are a whole new set of issues relating to evolution that must be considered. Standard genetic algorithms may not be appropriate for incremental evolution of robot controllers. Species adaptation genetic algorithms, (SAGA) have been developed to meet these special needs. The main cost of an evolutionary approach is the large number of trials that are required. Simulations-especially those involving vision in complex environments, or modeling detailed semiconductor physics—may not be adequate or practical. Examples of evolved robots will be discussed, including a specialized piece of equipment which allows a robot to be tested using simple vision in real time, and what is believed to be the first successful example of an evolved hardware controller for a robot.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02471110
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  • 13
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    Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs (1999)
    Springer
    Computational economics 13 (1999), S. 41-60 
    Details
    ISSN: 1572-9974
    Keywords: genetic algorithms ; learning ; equilibrium selection ; heterogeneous beliefs
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Economics
    Notes: Abstract We study a general equilibrium system where agents have heterogeneous beliefs concerning realizations of possible outcomes. The actual outcomes feed back into beliefs thus creating a complicated nonlinear system. Beliefs are updated via a genetic algorithm learning process which we interpret as representing communication among agents in the economy. We are able to illustrate a simple principle: genetic algorithms can be implemented so that they represent pure learning effects (i.e., beliefs updating based on realizations of endogenous variables in an environment with heterogeneous beliefs). Agents optimally solve their maximization problem at each date given their beliefs at each date. We report the results of a set of computational experiments in which we find that our population of artificial adaptive agents is usually able to coordinate their beliefs so as to achieve the Pareto superior rational expectations equilibrium of the model.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008610307810
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  • 14
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    Functional search in economics using genetic programming (1996)
    Springer
    Computational economics 9 (1996), S. 275-298 
    Details
    ISSN: 1572-9974
    Keywords: genetic programming ; genetic algorithms ; evolutionary search ; optimal growth ; econometrics ; nonparametric regression
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Economics
    Notes: Abstract This paper discusses economic applications of a recently developed artificial intelligence technique-Koza's genetic programming (GP). GP is an evolutionary search method related to genetic algorithms. In GP, populations of potential solutions consist of executable computer algorithms, rather than coded strings. The paper provides an overview of how GP works, and illustrates with two applications: solving for the policy function in a simple optimal growth model, and estimating an unusual regression function. Results suggest that the GP search method can be an interesting and effective tool for economists.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00119476
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  • 15
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    Efficient GA Based Techniques for Classification (1999)
    Springer
    Applied intelligence 11 (1999), S. 277-284 
    Details
    ISSN: 1573-7497
    Keywords: genetic algorithms ; classification ; data mining
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A common approach to evaluating competing models in a classification context is via accuracy on a test set or on cross-validation sets. However, this can be computationally costly when using genetic algorithms with large datasets and the benefits of performing a wide search are compromised by the fact that estimates of the generalization abilities of competing models are subject to noise. This paper shows that clear advantages can be gained by using samples of the test set when evaluating competing models. Further, that applying statistical tests in combination with Occam's razor produces parsimonious models, matches the level of evaluation to the state of the search and retains the speed advantages of test set sampling.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008386925927
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  • 16
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    Cooperative-Competitive Algorithms for Evolutionary Networks Classifying Noisy Digital Images (1999)
    Springer
    Neural processing letters 10 (1999), S. 223-229 
    Details
    ISSN: 1573-773X
    Keywords: artificial neural networks ; genetic algorithms ; evolutionary networks ; adaptive image processing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract We describe an efficient method of combining the global search of genetic algorithms (GAs) with the local search of gradient descent algorithms. Each technique optimizes a mutually exclusive subset of the network's weight parameters. The GA chromosome fixes the feature detectors and their location, and a gradient descent algorithm starting from random initial values optimizes the remaining weights. Three algorithms having different methods of encoding hidden unit weights in the chromosome are applied to multilayer perceptrons (MLPs) which classify noisy digital images. The fitness function measures the MLP classification accuracy together with the confidence of the networks.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018732508492
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  • 17
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    Two is better than one: A diploid genotype for neural networks (1996)
    Springer
    Neural processing letters 4 (1996), S. 149-155 
    Details
    ISSN: 1573-773X
    Keywords: adaptation ; diploidy ; genetic algorithms ; genotype-phenotype mapping ; neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In nature the genotype of many organisms exhibits diploidy, i.e., it includes two copies of every gene. In this paper we describe the results of simulations comparing the behavior of haploid and diploid populations of ecological neural networks living in both fixed and changing environments. We show that diploid genotypes create more variability in fitness in the population than haploid genotypes and buffer better environmental change; as a consequence, if one wants to obtain good results for both average and peak fitness in a single population one should choose a diploid population with an appropriate mutation rate. Some results of our simulations parallel biological findings.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00426023
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  • 18
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    A hybrid approach to vehicle routing using neural networks and genetic algorithms (1996)
    Springer
    Applied intelligence 6 (1996), S. 241-252 
    Details
    ISSN: 1573-7497
    Keywords: vehicle routing ; time windows ; neural networks ; genetic algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract A competitive neural network model and a genetic algorithm are used to improve the initialization and construction phase of a parallel insertion heuristic for the vehicle routing problem with time windows. The neural network identifies seed customers that are distributed over the entire geographic area during the initialization phase, while the genetic algorithm finds good parameter settings in the route construction phase that follows. Computational results on a standard set of problems are also reported.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00126629
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  • 19
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    A genetic algorithm for vehicle routing with backhauling (1996)
    Springer
    Applied intelligence 6 (1996), S. 345-355 
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    ISSN: 1573-7497
    Keywords: vehicle routing ; backhauling ; time windows ; genetic algorithms ; heuristics
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper, a greedy route construction heuristic for a vehicle routing problem with backhauling is described. This heuristic inserts customers one by one into the routes using a fixed a priori ordering of customers. Then, a genetic algorithm is used to identify an ordering that produces good routes. Numerical comparisons are provided with an exact algorithm and with other heuristic approaches.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00132738
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  • 20
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    Automatic Classification of Biological Particles from Electron-microscopy Images Using Conventional and Genetic-algorithm Optimized Learning Vector Quantization (1998)
    Springer
    Neural processing letters 8 (1998), S. 55-65 
    Details
    ISSN: 1573-773X
    Keywords: genetic algorithms ; neural networks ; neural network optimization ; image classification ; image reconstruction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Automatic classification of transmission electron-microscopy images is an important step in the complex task of determining the structure of biologial macromolecules. The process of 3D reconstruction from a set of such images implies their previous classification into homogeneous image classes. In general, different classes may represent either distinct biochemical specimens or specimens from different directions of an otherwise homogenous specimen. In this paper, a neural network classification algorithm has been applied to a real-data case in which it was known a priori the existence of two differentiated views of the same specimen. Using two labeled sets as a reference, the parameters and architecture of the classifier were optimized using a genetic algorithm. The global automatic process of training and optimization is implemented using the previously described g-lvq (genetic learning vector quantization) [10] algorithm, and compared to a non-optimized version of the algorithm, Kohonen's lvq (learning vector quantization) [7]. Using a part of the sample as training set, the results presented here show an efficient (approximately 90%) average classification rate of unknown samples in two classes. Finally, the implication of this kind of automatic classification of algorithms in the determination of three dimensional structure of biological particles is discused. This paper extends the results already presented in [11], and also improves them.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009617113191
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  • 21
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    Extracting Rules from Composite Neural Networks for Medical Diagnostic Problems (1998)
    Springer
    Neural processing letters 8 (1998), S. 253-263 
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    ISSN: 1573-773X
    Keywords: expert system ; genetic algorithms ; medical diagnosis ; neural networks ; rule extraction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Recently, neural networks have been applied to many medical diagnostic problems because of their appealing properties, robustness, capability of generalization and fault tolerance. Although the predictive accuracy of neural networks may be higher than that of traditional methods (e.g., statistical methods) or human experts, the lack of explanation from a trained neural network leads to the difficulty that users would hesitate to take the advise of a black box on faith alone. This paper presents a class of composite neural networks which are trained in such a way that the values of the network parameters can be utilized to generate If-Then rules on the basis of preselected meaningful coordinates. The concepts and methods presented in the paper are illustrated through one practical example from medical diagnosis.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009681803460
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  • 22
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    Testing real-time systems using genetic algorithms (1997)
    Springer
    Software quality journal 6 (1997), S. 127-135 
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    ISSN: 1573-1367
    Keywords: Keywords: testing ; real-time systems ; genetic algorithms ; temporal behaviour ; embedded systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The development of real-time systems is an essential industrial activity whose importance is increasing. The most important analytical method to assure the quality of real-time systems is dynamic testing. Testing is the only method which examines the actual run-time behaviour of real-time software, based on an execution in the real application environment. Dynamic aspects like the duration of computations, the memory actually needed, or the synchronization of parallel processes are of major importance for the correct function of real-time systems and have to be tested. A comprehensive investigation of existing software test methods shows that they mostly concentrate on testing for functional correctness. They are not suited for an examination of temporal correctness which is essential to real-time systems. Very small systems show a wide range of different execution times. Therefore, existing test procedures must be supplemented by new methods, which concentrate on determining whether the system violates its specified timing constraints. In general, this means that outputs are produced too early or their computation takes too long. The task of the tester is to find the inputs with the longest or shortest execution times to check whether they produce a temporal error. If the search for such inputs is interpreted as a problem of optimization, genetic algorithms can be used to find the inputs with the longest or shortest execution times automatically. The fitness function is the execution time measured in processor cycles. Experiments using genetic algorithms on a number of programs with up to 1511 LOC and 843 integer input parameters have successfully identified new longer and shorter paths than had been found using random testing or systematic testing. Genetic algorithms are able therefore to check large programs and they show considerable promise in establishing the validity of the temporal behaviour of real-time software.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018551716639
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  • 23
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    Metaheuristics for High School Timetabling (1998)
    Springer
    Computational optimization and applications 9 (1998), S. 275-298 
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    ISSN: 1573-2894
    Keywords: timetable problem ; tabu search ; simulated annealing ; genetic algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract In this paper we present the results of an investigation of the possibilities offered by three well-known metaheuristic algorithms to solve the timetable problem, a multi-constrained, NP-hard, combinatorial optimization problem with real-world applications. First, we present our model of the problem, including the definition of a hierarchical structure for the objective function, and of the neighborhood search operators which we apply to matrices representing timetables. Then we report about the outcomes of the utilization of the implemented systems to the specific case of the generation of a school timetable. We compare the results obtained by simu lated annealing, tabu search and two versions, with and without local search, of the genetic algorithm. Our results show that GA with local search and tabu search based on temporary problem relaxations both outperform simulated annealing and handmade timetables.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018354324992
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    The Slack Method: A New Method for Static Allocation of Hard Real-Time Tasks (1998)
    Springer
    Real-time systems 15 (1998), S. 103-130 
    Details
    ISSN: 1573-1383
    Keywords: allocation ; mapping ; multiprocessor scheduling ; parallel systems ; distributed systems ; simulated annealing ; genetic algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This article presents and evaluates the Slack Method, a new constructive heuristic for the allocation (mapping) of periodic hard real-time tasks to multiprocessor or distributed systems. The Slack Method is based on task deadlines, in contrast with other constructive heuristics, such as List Processing. The presented evaluation shows that the Slack Method is superior to list-processing-based approaches with regard to both finding more feasible solutions as well as finding solutions with better objective function values. In a comparative survey we evaluate the Slack Method against several alternative allocation techniques. This includes comparisons with optimal algorithms, non-guided search heuristics (e.g. Simulated Annealing), and other constructive heuristics. The main practical result of the comparison is that a combination of non-guided search and constructive approaches is shown to perform better than either of them alone, especially when using the Slack Method.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008092427865
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    Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms (1997)
    Springer
    Statistics and computing 7 (1997), S. 19-34 
    Details
    ISSN: 1573-1375
    Keywords: Bayesian networks ; genetic algorithms ; optimal decomposition ; graph triangulation ; moral graph ; NP-hard problems ; statistical analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine empirically the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm of Lauritzen and Spiegelhalter (1988) and is known to be NP-hard (Wen, 1991). We carry out experiments with distinct crossover and mutation operators and with different population sizes, mutation rates and selection biasses. The results are analysed statistically. They turn out to improve the results obtained with most other known triangulation methods (Kjærulff, 1990) and are comparable to results obtained with simulated annealing (Kjærulff, 1990; Kjærulff, 1992).
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018553211613
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    Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis (1998)
    Springer
    Artificial intelligence review 12 (1998), S. 265-319 
    Details
    ISSN: 1573-7462
    Keywords: genetic algorithms ; real coding ; continuous search spaces
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Genetic algorithms play a significant role, as search techniques forhandling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the naturalevolution principles of populations. These algorithms process apopulation of chromosomes, which represent search space solutions,with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006504901164
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    A Teaching Strategy for Memory-Based Control (1997)
    Springer
    Artificial intelligence review 11 (1997), S. 343-370 
    Details
    ISSN: 1573-7462
    Keywords: lazy learning ; nearest neighbor ; genetic algorithms ; differential games ; pursuit games ; teaching ; reinforcement learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Combining different machine learning algorithms in the same system can produce benefits above and beyond what either method could achieve alone. This paper demonstrates that genetic algorithms can be used in conjunction with lazy learning to solve examples of a difficult class of delayed reinforcement learning problems better than either method alone. This class, the class of differential games, includes numerous important control problems that arise in robotics, planning, game playing, and other areas, and solutions for differential games suggest solution strategies for the general class of planning and control problems. We conducted a series of experiments applying three learning approaches – lazy Q-learning, k-nearest neighbor (k-NN), and a genetic algorithm – to a particular differential game called a pursuit game. Our experiments demonstrate that k-NN had great difficulty solving the problem, while a lazy version of Q-learning performed moderately well and the genetic algorithm performed even better. These results motivated the next step in the experiments, where we hypothesized k-NN was having difficulty because it did not have good examples – a common source of difficulty for lazy learning. Therefore, we used the genetic algorithm as a bootstrapping method for k-NN to create a system to provide these examples. Our experiments demonstrate that the resulting joint system learned to solve the pursuit games with a high degree of accuracy – outperforming either method alone – and with relatively small memory requirements.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1006597715165
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    Evolution-Based Methods for Selecting Point Data for Object Localization: Applications to Computer-Assisted Surgery (1998)
    Springer
    Applied intelligence 8 (1998), S. 7-19 
    Details
    ISSN: 1573-7497
    Keywords: object localization ; registration ; genetic algorithms ; population-based incremental learning ; computer-assisted surgery
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Object localization has applications in many areas of engineering and science. The goal is to spatially locate an arbitrarily shaped object. In many applications, it is desirable to minimize the number of measurements collected while ensuring sufficient localization accuracy. In surgery, for example, collecting a large number of localization measurements may either extend the time required to perform a surgical procedure or increase the radiation dosage to which a patient is exposed. Localization accuracy is a function of the spatial distribution of discrete measurements over an object when measurement noise is present. In previous work (J. of Image Guided Surgery, Simon et al., 1995), metrics were presented to evaluate the information available from a set of discrete object measurements. In this study, new approaches to the discrete point data selection problem are described. These include hillclimbing, genetic algorithms (GAs), and Population-Based Incremental Learning (PBIL). Extensions of the standard GA and PBIL methods that employ multiple parallel populations are explored. The results of extensive empirical testing are provided. The results suggest that a combination of PBIL and hillclimbing result in the best overall performance. A computer-assisted surgical system that incorporates some of the methods presented in this paper is currently being evaluated in cadaver trials.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008230929638
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    Evolving the Topology and the Weights of Neural Networks Using a Dual Representation (1998)
    Springer
    Applied intelligence 8 (1998), S. 73-84 
    Details
    ISSN: 1573-7497
    Keywords: Evolutionary computation ; genetic algorithms ; neural networks ; genetic programming
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Evolutionary computation is a class of global search techniques based on the learning process of a population of potential solutions to a given problem, that has been successfully applied to a variety of problems. In this paper a new approach to the construction of neural networks based on evolutionary computation is presented. A linear chromosome combined to a graph representation of the network are used by genetic operators, which allow the evolution of the architecture and the weights simultaneously without the need of local weight optimization. This paper describes the approach, the operators and reports results of the application of this technique to several binary classification problems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008272615525
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    Developing Mobile Robot Wall-Following Algorithms Using Genetic Programming (1998)
    Springer
    Applied intelligence 8 (1998), S. 33-41 
    Details
    ISSN: 1573-7497
    Keywords: genetic programming ; genetic algorithms ; computational genetics ; machine learning ; adaptive systems ; mobile robot ; robotics ; robot ; wall-following
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper demonstrates the use of genetic programming (GP) for the development of mobile robot wall-following behaviors. Algorithms are developed for a simulated mobile robot that uses an array of range finders for navigation. Navigation algorithms are tested in a variety of differently shaped environments to encourage the development of robust solutions, and reduce the possibility of solutions based on memorization of a fixed set of movements. A brief introduction to GP is presented. A typical wall-following robot evolutionary cycle is analyzed, and results are presented. GP is shown to be capable of producing robust wall-following navigation algorithms that perform well in each of the test environments used.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008216530547
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    Evolving Neuro-Controllers for a Dynamic System Using Structured Genetic Algorithms (1998)
    Springer
    Applied intelligence 8 (1998), S. 113-121 
    Details
    ISSN: 1573-7497
    Keywords: genetic algorithms ; neural networks ; pole-cart system ; neuro-controller ; simulation ; gene activation ; multi-level chromosome
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract This paper describes the application of the Structured Genetic Algorithm (sGA) to design neuro-controllers for an unstable physical system. In particular, the approach uses a single unified genetic process to automatically evolve complete neural nets (both architectures and their weights) for controlling a simulated pole-cart system. Experimental results demonstrate the effectiveness of the sGA-evolved neuro-controllers for the task—to keep the pole upright (within a specified vertical angle) and the cart within the limits of the given track.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008291923124
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    Fractal Fitness Landscape and Loss of Robustness in Evolutionary Robot Navigation (1998)
    Springer
    Autonomous robots 5 (1998), S. 199-213 
    Details
    ISSN: 1573-7527
    Keywords: adaptive behaviors ; evolutionary robots ; fractal fitness landscape ; robot navigation ; genetic algorithms ; over-adaptation ; developed neural network
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Abstract An autonomous robot “Khepera” was simulated with a sensory-motor model, which evolves in the genetic algorithm (GA) framework, with the fitness evaluation in terms of the navigation performance in a maze course. The sensory-motor model is a developed neural network decoded from a graph-represented chromosome, which is evolved in the GA process with several genetic operators. It was found that the fitness landscape is very rugged when it is observed at the starting point of the course. A hypothesis for this ruggedness is proposed, and is supported by the measurement of fractal dimension. It is also observed that the performance is sometimes plagued by “Loss of Robustness,” after the robot makes major evolutionary jumps. Here, the robustness is quantitatively defined as a ratio of the averaged fitness of the evolved robot navigating in perturbed environments over the fitness of the evolved robot in the referenced environment. Possible explanation of robustness loss is the over-adaptation occurred in the environment where the evolution was taken place. Testing some other possibilities for this loss of robustness, many simulation experiments were conducted which smooth out the discrete factors in the model and environment. It was found that smoothing the discrete factors does not solve the loss of robustness. An effective method for maintaining the robustness is the use of averaged fitness over different navigation conditions. The evolved models in the simulated environment were tested by down-loading the models into the real Khepera robot. It is demonstrated that the tendency of fitness values observed in the simulation were adequately regenerated.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008874222544
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