Stefan Voget and Michael Kolonko. Multidimensional Optimization with a Fuzzy Genetic Algorithm. Journal of Heuristics, 4(3):221--244, September 1998. Home/Search Document Details and Download
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Handling Preferences in Evolutionary Multiobjective Optimization: .. - Coello (2000) (2 citations) (Correct)
....an essential role in the procedures, only have a technical character, and their influence on the results is not always well understood. Moreover, in some outranking approaches, the notion of degree of credibility is rather difficult for practitioners [5] 5. Fuzzy Logic: Voget and Kolonko [47] used a fuzzy controller that automatically regulates the selection pressure of an EA by using a set of predefined goals that define the desirable behavior of the population. Although the approach was used only to keep diversity in the population, it could easily be extended to incorporate ....
Stefan Voget and Michael Kolonko. Multidimensional Optimization with a Fuzzy Genetic Algorithm. Journal of Heuristics, 4(3):221--244, September 1998.
An Updated Survey of GA-Based Multiobjective Optimization.. - Coello (1998) (22 citations) (Correct)
....so that the GA constraints the search to that specific area. Additionally, Greenwood et al. 1997] defined a certain metrics that allows us to obtain a single value (or utility function) that will guide the search to the particular Pareto region that is of interest to the decision maker. Finally, Voget and Kolonko [1998] proposed the use of a fuzzy controller that regulates the selection pressure automatically by using a set of predefined goals that define the desirable behavior of the population. An interesting aspect of this work is that they actually combine Pareto ranking with VEGA during the same run of ....
Voget, S. and Kolonko, M. 1998. Multidimensional Optimization with a Fuzzy Genetic Algorithm. Journal of Heuristics . (In Press).
A Comprehensive Survey of Evolutionary-Based Multiobjective.. - Coello (1998) (75 citations) (Correct)
....constrains the search to that specific area. Additionally, Greenwood et al. 30] defined a certain metrics that allows us to obtain a single value (or utility function) that will guide the search to the particular Pareto region that is of interest to the decision maker. Finally, Voget and Kolonko [100] proposed the use of a fuzzy controller that regulates the selection pressure automatically by using a set of predefined goals that define the desirable behavior of the population. An interesting aspect of this work is that they actually combine Pareto ranking with VEGA during the same run of ....
....by using a set of predefined goals that define the desirable behavior of the population. An interesting aspect of this work is that they actually combine Pareto ranking with VEGA during the same run of the GA, to allow the desired reduction of deviations from the goals specified by the authors [100]. These 3 proposals are quite interesting, but still more work needs to be done in this area, preferrently with real world problems (Fonseca s approach was an appropriate choice for the optimization of a gas turbine engine [17] and Greenwood et al. 30] showed that their approach performed well ....
S. Voget and M. Kolonko. Multidimensional Optimization with a Fuzzy Genetic Algorithm. Journal of Heuristics, 1998. (In Press).
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