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1887

Second International Conference on Genetic Algorithms in Engineering Systems

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  • Location: Glasgow, UK
  • Conference date: 2-4 Sept. 1997
  • ISBN: 0 85296 693 8
  • Conference number: CP446
  • The following topics are dealt with: multiobjective algorithms; optimal control; engineering systems; evolutionary computation; signal processing; utilities; scheduling; robotics and automation; manufacturing scheduling; controller design; route and network planning; and PID control

1 - 20 of 88 items found

  • Optimising object recognition parameters using a parallel multiobjective genetic algorithm
  • Automatic synthesis of active electric networks using genetic algorithms
  • Soft computing techniques on automatic synthesis of greenhouse climate controllers
  • The use of a biased heuristic by a genetic algorithm applied to the design of multipoint connections in a local access network
  • Optimal reactive power dispatch using an adaptive genetic algorithm
  • Parallel genetic algorithms for optimised fuzzy modelling with application to a fermentation process
  • Dynamic local search
  • Multi-objective optimisation of distributed active magnetic bearing controllers
  • Optimization of roll profiles in the hot rolling of wide steel strip
  • An evolutionary paradigm for designing fuzzy rule-based systems from examples
  • Solving the TSP problem with a new model in evolutionary computation
  • Benchmarking the restoration of heavily loaded networks using a two dimensional order-based genetic algorithm
  • Solving large travelling salesman problems with small populations
  • A genetic algorithm for learning profiles in broadcasting applications on the Internet
  • Design of low complexity FIR filters using genetic algorithms and directed graphs
  • A genetic algorithm for the high-level synthesis of DSP systems for low power
  • Genetic algorithms in automatic fire detection technology
  • Vibration data compression with optimal wavelet coefficients
  • Assessing the convergence of rank-based multiobjective genetic algorithms
  • Constrained genetic operators preserving feasibility of solutions in genetic algorithms
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