Wilson, P. and M. Macleod (1993). Low implementation cost iir digital filter design using genetic algorithms. IEE/IEEE workshop on Natural Algorithms in Signal Processing , 1--8.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... Traditionally, there are several methods available in the Operational Research (OR) literature for solving MOPs as mathematical programming models, viz goal programming (Charnes and Cooper 1961) weighted sum method (Turban and Meredith 1994) goals as requirement (Coello 1999) goal attainment (Wilson and Macleod 1993), and the iso resource cost solution method (Zeleny 1998) The concept of a goal is somewhat di#erent from an objective. A goal is usually considered as a planned objective. Therefore, the optimality is measured, in the case of goal based methods, in terms of the amount of deviation from the ....

Wilson, P. and M. Macleod (1993). Low implementation cost iir digital filter design using genetic algorithms. IEE/IEEE workshop on Natural Algorithms in Signal Processing , 1--8.

.... m etaheuristiques : ffl Algorithmes g en etiques : les algorithmes g en etiques ont et e utilis es pour r esoudre plusieurs PMO transform es en un probl eme uni objectif : ordonnancement [83] planification de robots [44] g en eration de structures chimiques [46] conception de filtres IIR [99], placement [49] transport [100] En plus de la repr esentation d une solution du probl eme dans le codage d un individu, Hajela et Lin [35] ont inclu les poids de chaque objectif dans les chromosomes. Ils encouragent leur diversit e dans la population a travers une fonction de partage (voir ....

....Pareto optimale. La valeur optimale de ff indique si les buts sont atteignables ou non. Une valeur n egative de ff implique que les buts sont atteignables. Sinon, si ff 0, le but n est pas atteignable. Le probl eme formul e ainsi a et e r esolu en utilisant les algorithmes g en etiques [99]. L inconv enient majeur de cette m ethode est l absence eventuelle de la pression de s election des solutions g en er ees. Par exemple, si on a deux solutions qui ont la meme valeur pour un objectif et des valeurs diff erentes pour l autre objectif, elles ont le meme cout. L algorithme de ....

P. B. Wilson and M. D. Macleod. Low implementation cost iir digital filter design using genetic algorithms. In IEE/IEEE Workshop on Natural Algorithms in Signal Processing, pages 4/1--4/8, Chelmsford, UK, 1993.

....methods to IIR filter design is attractive, since in many applications a global optimal solution can be much better than local optimal ones. When considering global optimisation methods for IIR filter design, the genetic algorithm (GA) 3, 4, 5] seems to have attracted the main attention [6, 7, 8]. Simulated annealing (SA) 9, 10, 11] by contrast has not received similar interests. The ASA [12, 13, 14, 15] an improved version of SA, is known to provide significant improvement in convergence speed over standard versions of SA. This study investigates the use of the ASA to IIR system ....

Wilson, P.B., and Macleod, M.D. Low implementation cost IIR digital filter design using genetic algorithms. In Workshop on Natural Algorithms in Signal Processing, Chelmsford, Essex, 1993, pp.4/1--4/8.

....JN (wH ) 1 N N X k=1 (d(k) Gamma y(k) 2 : 28) A major concern in IIR filtering applications is that the cost function of IIR filters is generally multimodal, and a gradient based algorithm can easily be stuck at local minima. The GA has been applied to IIR filter design (e.g. 8] [20], 21] to overcome this difficulty. We demonstrate that the ASA offers an alternative to IIR filter design. To maintain the stability during optimization, we convert the direct form coefficients b i , 1 i M , to the lattice form reflection coefficients i , 0 i M Gamma 1, and make sure that ....

P.B. Wilson and M.D. Macleod, "Low implementation cost IIR digital filter design using genetic algorithms," in Workshop on Natural Algorithms in Signal Processing (Chelmsford, Essex), Nov.14-16, 1993, pp.4/1--4/8.

....to IIR filter design is attractive,since in many applications a global optimal solution can be much better than local optimal ones. When considering global optimisation methods for digital IIR filter design, the genetic algorithm (GA) 3, 4, 5] seems to have attracted considerable attention [6, 7, 8]. Simulated annealing (SA) 9, 10, 11] by contrast has not received similar interests in this application. 1 2 Please write authorrunninghead Author Name(s) in file SA represents a general global optimisation technique with some strikingly positive and negative features. An attractive ....

Wilson, P.B., and Macleod, M.D. Low implementation cost IIR digital filter design using genetic algorithms. In Workshop on Natural Algorithms in Signal Processing, Chelmsford, Essex, 1993, pp.4/1--4/8.

....Genetic Algorithms, IEEE Transactions on Microwave Theory and Techniques, 41 (6 7) 1024 1031 (1993) 102] Michielssen, Eric and Daniel S. Weile. Genetic Algorithms in Engineering and Computer Science, chapter Electromagnetic System Design Using Genetic Algorithms, 345 369. In Winter et al. [160], 1995. 103] Miller, George A. The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information, The Psychological Review , 63 (2) 81 97 (1956) 104] Mohammed, O. A. and G. F. Uler. Genetic Algorithms for the Optimal Design of Electromagnetic Devices. ....

....in Engineering Design and Control . September 1994. 120] Pohlheim, Hartmut. Genetic and Evolutionary Algorithm Toolbox for use with MATLAB . Technical Report, Technical University Ilmenau, 1998. 121] Poloni, Carlo. Genetic Algorithms in Engineering and Computer Science. In Winter et al. [160], 397 416. 122] Porto, William, editor. Proceedings of the 1997 (4th) IEEE International Conference on Evolutionary Computation, Piscataway, NJ: IEEE, April 1997. 82 REFERENCES REFERENCES [123] Ritzel, Brian J. et al Using Genetic Algorithms to Solve a Multiple Objective Groundwater ....

Wilson, P. B. and M. D. Macleod. "Low Implementation Cost IIR Digital Filter Design Using Genetic Algorithms." IEE/IEEE Workshop on Natural Algorithms in Signal Processing1 . 4/1 -- 4/8. Chelmsford, UK: IEE, 1993.

.... Several applications of evolutionary algorithms in the optimization of aggregating functions have been reported in the literature, from the simple weighted sum approach (Jakob et al. 1992) to target vector optimization (Wienke et al. 1992) Goal attainment, among other methods, was used by Wilson and Macleod (1993), who also monitored the population for non dominated solutions. Handling constraints with penalty functions is yet another example of an additive aggregating function. The fact that penalty functions are generally problem dependent and, as a consequence, difficult to set has prompted the ....

Wilson, P. B. and Macleod, M. D. (1993). Low implementation cost IIR digital filter design using genetic algorithms. In IEE/IEEE Workshop on Natural Algorithms in Signal Processing, volume 1, pages 4/1--4/8, Chelmsford, U.K.

....possible, avoiding certain obstacles and aiming to produce a path as smooth and short as possible. Jones et al. 8] used weights for their genetic operators in order to reflect their effectiveness when a GAwas applied to generate hyperstructures from a set of chemical structures. Wilson Macleod [9] used this approach as one of the methods incorporated into a GA to design multiplierless IIR filters in which the two conflicting objectives were to minimize the response error and the implementation cost of the filter. Liu et al. 10] used this technique to optimize the layout and actuator ....

....use in practice. 4.6 Target Vector Approaches Under this name we will consider approaches in which the decision maker has to assign targets or goals that he she wishes to achieve for each objective. The most popular techniques included here are: Goal Programming [48, 49] Goal Attainment [9, 50] and the min max approach [51, 52, 53] These techniques will yield a dominated solution if the goals desired are chosen in the feasible domain, which is a condition that might certainly limit their applicability. 4.6.1 Applications Wilson MacLeod [9] used goal attainment as another of the ....

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P. B. Wilson and M. D. Macleod. Low implementation cost IIR digital filter design using genetic algorithms. In IEE/IEEE Workshop on Natural Algorithms in Signal Processing, pages 4/1--4/8, Chelmsford, U.K., 1993.

....as a good compromise, tuning of the aggregating function may be required, followed by new runs of the optimizer, until a suitable solution is found. As a workaround, of the many candidate solutions evaluated in a single run of the EA, those non dominated solutions may provide valuable alternatives [12, 13]. However, since the algorithm sees such alternatives as sub optimal, they cannot be expected to be optimal in any sense. Aggregating functions have been widely used with EAs, from the simple weighted sum approach, e.g. 14] to target vector optimization [15] An implementation of goal ....

....sub optimal, they cannot be expected to be optimal in any sense. Aggregating functions have been widely used with EAs, from the simple weighted sum approach, e.g. 14] to target vector optimization [15] An implementation of goal attainment, among other methods, was used by Wilson and Macleod [12]. 4.2.1 Non Pareto approaches Treating objectives separately was first proposed by Schaffer [16] as a move towards finding multiple non dominated solutions with a single algorithm run. In his approach, known as the Vector Evaluated Genetic Algorithm (VEGA) appropriate fractions of the next ....

P. B. Wilson and M. D. Macleod, "Low implementation cost IIR digital filter design using genetic algorithms," in IEE/IEEE Workshop on Natural Algorithms in Signal Processing, vol. 1, (Chelmsford, U.K.), pp. 4/1--4/8, 1993.

....avoiding certain obstacles and aiming to produce a path as smooth and short as possible. Jones et al. 42] used weights for their genetic operators in order to reflect their effectiveness when a GA was applied to generate hyperstructures from a set of chemical structures. Wilson Macleod [103] used this approach as one of the methods incorporated into a GA to design multiplierless IIR filters in which the two conflicting objectives were to minimize the response error and the implementation cost of the filter. Liu et al. 51] used this technique to optimize the layout and actuator ....

....inform the decision maker of whether the goals are attainable or not. A negative value of ff implies that the goal of the decision maker is attainable and an improved solution will be obtained. Otherwise, if ff 0, then the decision maker goal is unattainable. Applications Wilson MacLeod [103] used this approach as another of the methods incorporated into their GA to design multiplierless IIR filters. Strengths and Weaknesses As Wilson and MacLoud [103] indicate, goal attainment has several weaknesses, from which probably the main one is the misleading selection pressure that it can ....

[Article contains additional citation context not shown here]

P. B. Wilson and M. D. Macleod. Low implementation cost IIR digital filter design using genetic algorithms. In IEE/IEEE Workshop on Natural Algorithms in Signal Processing, pages 4/1--4/8, Chelmsford, U.K., 1993.

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