By Topic

IEE Colloquium on Genetic Algorithms for Control Systems Engineering

28 May 1993

Filter Results

Displaying Results 1 - 11 of 11
  • Multiobjective genetic algorithms

    Publication Year: 1993, Page(s):6/1 - 6/5
    Cited by:  Papers (12)  |  Patents (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (244 KB)

    Multiobjective genetic algorithms (MOGAs) are introduced as a modification of the standard genetic algorithm at the selection level. Rank-based fitness assignment and the implementation of sharing in the objective value domain are two of the important aspects of this class of algorithms. The ability of the decision maker (DM) to progressively articulate its preferences while learning about the pro... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • IEE Colloquium on `Genetic Algorithms for Control Systems Engineering' (Digest No. 1993/130)

    Publication Year: 1993
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (12 KB)

    The following topics were dealt with: algorithm optimal population; systems identification; robot trajectory planning and collision avoidance; multiobjective genetic algorithms; parallel genetic algorithms; classifier systems; distributed multivariable fuzzy control; and AI in distributed control View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Designing embedded parallel systems with parallel genetic algorithms

    Publication Year: 1993, Page(s):7/1 - 7/2
    Cited by:  Papers (1)  |  Patents (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (132 KB)

    Generic parallel genetic algorithms are developed with reference to the example of the real-time path planning problem for mobile robots. Most robot motion planners are used off-line: the planner is invoked with a model of the environment, it produces a path which is passed to the robot controller which in turn executes it. In general, the time necessary to achieve this loop is not short enough to... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Genetic algorithms in control systems engineering: a brief introduction

    Publication Year: 1993, Page(s):1/1 - 1/5
    Cited by:  Papers (9)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (296 KB)

    Genetic algorithms (GA) are adaptive search techniques, based on the principles of natural genetics and natural selection, which, in control systems engineering, can be used as an optimization tool or as the basis of more general adaptive systems. Following an introduction to the simple GA, important characteristics of GA are identified and control applications are described View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Generation of collision-free paths, a genetic approach

    Publication Year: 1993, Page(s):5/1 - 5/6
    Cited by:  Papers (3)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (232 KB)

    This paper deals with the generation of minimum distance paths for a robot manipulator operating in an environment cluttered with obstacles. These paths are optimised using a genetic approach. The genetic algorithm objective function is formulated in an obstacle avoidance problem context. Evaluation of the genetic algorithm parameters and their behaviour is undertaken in order to determine the mos... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Genetic-based agents for control of distributed systems

    Publication Year: 1993, Page(s):10/1 - 10/4
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (232 KB)

    Management and control of distributed systems are hard tasks for conventional control techniques. Distributed computer control systems (DCCS) and data communication technology have helped to alleviate some of the problems of distributed systems. However, there are some problems that are difficult to solve using conventional methods. Artificial intelligence (AI) techniques have been proposed as sol... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Genetic-based minimum-time trajectory planning of articulated manipulators with torque constraints

    Publication Year: 1993, Page(s):4/1 - 4/3
    Cited by:  Papers (1)  |  Patents (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (184 KB)

    This paper is concerned with introducing a genetic-based algorithm for the minimum-time trajectory planning of articulated robotic manipulators. The planning procedure is performed in the configuration space and respects all physical constraints imposed on the manipulator design, including the limits on the torque values applied to the motor of each joint of the arm; consequently, the complete non... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A distributed genetic algorithm for multivariable fuzzy control

    Publication Year: 1993, Page(s):9/1 - 9/3
    Cited by:  Patents (12)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (156 KB)

    The traditional approach to multiple parameter optimization in genetic algorithm (GA) practice is to combine the coding of the parameters into a single compound bit-string; the so-called concatenated binary mapping. This approach has some shortcomings; the GA is a competition-based technique that has a natural tendency to evolve one winner which in complex problems yields a solution that is better... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Systems identification with genetic algorithms

    Publication Year: 1993, Page(s):3/1 - 3/3
    Cited by:  Papers (1)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (116 KB)

    Genetic algorithms are applied to the identification of black-box systems and partially known systems. The approach is best suited to the partially known systems (PKS) problem; in contrast to least-squares-based algorithms for identification of linear black-box systems, corresponding algorithms for identification of partially known systems are in the early stages of development. The best known alg... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Classifier systems for control

    Publication Year: 1993, Page(s):8/1 - 8/3
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (136 KB)

    Classifier systems lie midway between neural networks and symbolic processing systems and potentially combine the benefits of both. They are parallel message-passing rule-based systems which use genetic algorithms to discover new rules as well as providing for reinforcement learning and programming. It has been proposed that a suitable application of genetic algorithms is to evolve robots. A most ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Optimal population size for genetic algorithms: an investigation

    Publication Year: 1993, Page(s):2/1 - 2/4
    Cited by:  Papers (2)
    IEEE is not the copyright holder of this material | Click to expandAbstract | PDF file iconPDF (128 KB)

    The performance of genetic algorithms (GAs) is affected by the parameters that are employed. In particular, the population size affects the performance and efficiency of GA-based systems. Grefenstette (1986) claimed that a population size between 60-110 is optimal for the convergence of GA-based systems to optimal solution. This paper presents studies that do not support this claim. GAPOLE, a GA-b... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.