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Table 1: The specific settings for each of the ZDT test functions used in this study, and comments on their Pareto optimal fronts. All objective functions are to be minimised.

in An Evolution Strategy with Probabilistic Mutation for Multi-Objective Optimisation
by Simon Huband, Phil Hingston, et al. 2003
Cited by 5

Table 1: The specific settings for each of the ZDT test functions used in this study, and comments on their Pareto optimal fronts. All objective functions are to be minimised.

in An evolution strategy with probabilistic mutation for multi-objective optimisation
by Simon Hub, Phil Hingston 2003
Cited by 5

Table 7. Properties of the WFG problems. All WFG problems are scalable, have no extremal nor medial parameters, have dissimilar parameter domains and Pareto optimal tradeofi magnitudes, have known Pareto optimal sets, and can be made to have a distinct many-to-one mapping from the Pareto optimal set to the Pareto optimal front by scaling the number of position parameters.

in A Scalable Multi-objective Test Problem Toolkit
by Simon Hub, Luigi Barone, Lyndon While, Phil Hingston 2005

Table 2: Effect of function g on the test problem. Function g#28x m+1 ;:::;x N #29 (#3E0), say n = N , m Controls search space lateral to the Pareto-optimal front Type Example and Effect

in Multi-Objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems
by Kalyanmoy Deb 1999
Cited by 112

Table 1: Distribution of genotypically and phenotypically different solutions in the Pareto-optimal front with same values in both objectives. n1;BBs refers to the number of k-bit partitions (substructures) with 1s and n0;BBs is the number of k-bit partitions with 0s.

in Limits of scalability of multiobjective estimation of distribution algorithms
by Kumara Sastry, Martin Pelikan, David E. Goldberg 2005
Cited by 2

Table 1. Details of interesting NNs from the \meta Pareto front quot;. The rst three approximate Pareto optimal solutions were found with selection method (A), all other with variant (B).

in Evolutionary multi-objective optimization of neural networks for face detection
by Stefan Wiegand, Christian Igel, Uwe Handmann 2004
Cited by 3

Table 2. Pareto-Optimal Solutions

in Flexibility/Cost-Tradeoffs of Platform-Based Systems
by Christian Haubelt, Jürgen Teich, Kai Richter, Rolf Ernst
"... In PAGE 17: ...ow, we continue with the next possible resource allocation, i.e., P1. Due to space limitations, we only present the results. The set of Pareto-optimal so- lutions for this example is shown in Table2 . At the beginning, our search space consisted of 225 design points.... ..."

Table 11 Pareto-optimal results

in lead-time and dynamic demand
by Jayanta Kumar Dey A, Samarjit Kar B, Manoranjan Maiti C 2003

Table 5. Aerodynamic coe cients and endurance factor values for selected Pareto optimal solutions

in unknown title
by unknown authors
"... In PAGE 8: ... Figure 8 shows selected Pareto front airfoils and the laminar-turbulent pressure distributions. Table5 lists the coe cients of lift and drag for the selected solutions contained in the Pareto front. Interesting trade-o s between fully turbulent and laminar-turbulent designs can be understood through this Pareto front.... ..."

Table 3. Example design configurations of Example 1

in MOMS-GA: A Multiobjective Multi-State Genetic Algorithm for System Reliability Optimization Design Problems
by Heidi A. Taboada, Jose F. Espiritu, David W. Coit 2007
"... In PAGE 15: ...he knee region (Das, 1999; Branke et al., 2004) are presented as good compromises. The knee is formed by those solutions of the Pareto-optimal front where a small improvement in one objective would lead to a large deterioration in at least one other objective. Table3... ..."
Cited by 1
Results 1 - 10 of 1,532