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Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization
, 1993
"... The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to a ..."
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Cited by 439 (12 self)
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The paper describes a rankbased fitness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory for setting the niche size is presented. The fitness assignment method is then modified to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seen as the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a satisfactory solution to the problem. Illustrative results of how the DM can interact with the genetic algorithm are presented. They also show the ability of the MOGA to uniformly sample regions of the tradeoff surface.
Multicriteria Decision Making and Evolutionary Computation
, 1996
"... Applying evolutionary computation (EC) to multicriteria decision making addresses two difficult problems: (1) searching intractably large and complex spaces and (2) deciding among multiple objectives. Both of these problems are open areas of research, but relatively little work has been done on the ..."
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Cited by 14 (0 self)
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Applying evolutionary computation (EC) to multicriteria decision making addresses two difficult problems: (1) searching intractably large and complex spaces and (2) deciding among multiple objectives. Both of these problems are open areas of research, but relatively little work has been done on the COMBINED problem of searching large spaces to meet multiple objectives. While multicriteria decision analysis usually assumes a small number of alternative solutions to choose from, or an "easy" (e.g., linear) space to search, research on robust search methods generally assumes some way of aggregating multiple objectives into a single figure of merit. This traditional separation of search and multicriteria decisions allows for two straightforward hybrid strategies: (1) make multicriteria decisions FIRST, to aggregate objectives, then apply EC search to optimize the resulting figure of merit, or (2) conduct multiple EC searches FIRST using different aggregations of the objectives in order to o...
RCS multiobjective optimization of scattered waves by active control elements using GAs
"... The problem of finding an optimal distribution of active control elements in order to minimize the backscattering of a reflector in Computational Electromagnetics is twofold. The optimization process must take into account both the distribution of the active elements on the surface of the obstacle a ..."
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Cited by 2 (1 self)
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The problem of finding an optimal distribution of active control elements in order to minimize the backscattering of a reflector in Computational Electromagnetics is twofold. The optimization process must take into account both the distribution of the active elements on the surface of the obstacle and the value of the phase and amplitude for each active element . The distribution problem is a typical combinatorial problem whereas the phase and amplitude optimization is a continuous problem. Both problems are solved by means of Genetic Algorithms via a fitness evaluation corresponding to the Radar Cross Section (RCS) signature of the radar illumination. The second part of our application deals with multiobjective optimization, using both a Pareto and a Nash approaches. The aim is to find a distribution of active elements which is optimal for several radar illuminations. The Pareto multiobjective optimization is a cooperative approach whereas the Nash multiobjective optimization is no...
F1.12: Multicriteria Decision Making and Evolutionary Computation
, 1996
"... Applying evolutionary computation (EC) to multicriteria decision making addresses two difficult problems: (1) searching intractably large and complex spaces and (2) deciding amongmultiple objectives. Both of these problems are open areas of research, but relatively little work has been done on the C ..."
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Cited by 2 (0 self)
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Applying evolutionary computation (EC) to multicriteria decision making addresses two difficult problems: (1) searching intractably large and complex spaces and (2) deciding amongmultiple objectives. Both of these problems are open areas of research, but relatively little work has been done on the COMBINED problem of searching large spaces to meet multiple objectives. While multicriteria decision analysis usually assumes a small number of alternative solutions to choose from, or an "easy" (e.g., linear) space to search, research on robust search methods generally assumes some way of aggregating multiple objectives into a single figure of merit. This traditional separation of search and multicriteria decisions allows for two straightforward hybrid strategies: (1) make multicriteria decisions FIRST, to aggregate objectives, then apply EC search to optimize the resulting figure of merit, or (2) conduct multiple EC searches FIRST using different aggregations of the objectives in order to o...