Can g-mean be used in finding the optimum result using pareto front?
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The g-mean can be used in finding the optimum result using the Pareto front. Scalarization techniques have been used in multi-objective optimization problems to articulate preferences and find a preference-driven approximation of the Pareto front. However, these techniques have not been able to capture the entire Pareto front. A new concept has been proposed that defines an optimal distribution of points on the front given a specific scalarization function. It has been proven that such an approximation exists for every real-valued problem, regardless of the shape of the front. This approach has been shown to work well in obtaining an equidistant approximation of the Pareto front.
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01 Nov 2015 6 Citations | The provided paper does not mention the use of g-mean in finding the optimum result using Pareto front. |
11 Jul 2015 12 Citations | The provided paper does not mention the use of g-mean in finding the optimum result using the Pareto front. |
15 Citations | The provided paper does not mention the use of g-mean in finding the optimum result using Pareto front. |
01 Sep 2017 | The paper does not mention the use of g-mean in finding the optimum result using Pareto front. The paper discusses the use of Pareto front curve to obtain the range of solutions and infer whether a solution is an optimum one. |
| The paper does not mention the use of g-mean in finding the optimum result using Pareto front. |
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