Comparing solution sets of different size in evolutionary many-objective optimization
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- Type
- article
- Published
- 2015-05-25
- Cited by
- 9
- References
- 35
- OpenAlex
- https://openalex.org/W1499764069
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:5936019
Keywords
Population size, Set (abstract data type), Population, Evolutionary algorithm, Computer science
References
- SPEA2: Improving the strength pareto evolutionary algorithm
- Scalable multi-objective optimization test problems
- Single-objective and multi-objective formulations of solution selection for hypervolume maximization
- Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization
- Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization
- A survey on multi-objective evolutionary algorithms for many-objective problems
- Iterative approach to indicator-based multiobjective optimization
- Meta-level multi-objective formulations of set optimization for multi-objective optimization problems: multi-reference point approach to hypervolume maximization
- Selecting a small number of non-dominated solutions to be presented to the decision maker
- A study on population size and selection lapse in many-objective optimization
- An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
- Evolutionary many-objective optimization: A short review
- A Grid-Based Evolutionary Algorithm for Many-Objective Optimization
- An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach
- Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
- HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization
- Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms
- A fast and elitist multiobjective genetic algorithm: NSGA-II
- Behavior of Multiobjective Evolutionary Algorithms on Many-Objective Knapsack Problems
- Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization
Cited by
- Relation Between Weight Vectors and Solutions in MOEA/D
- Enhanced decomposition-based many-objective optimization using supplemental weight vectors
- How to compare many-objective algorithms under different settings of population and archive sizes
- Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios
- An analysis of control parameters of MOEA/D under two different optimization scenarios
- A hybridized angle-encouragement-based decomposition approach for many-objective optimization problems
- A niching indicator-based multi-modal many-objective optimizer
- Identifying Pareto Fronts Reliably Using a Multistage Reference-Vector-Based Framework
- The Impact of Population Size, Number of Children, and Number of Reference Points on the Performance of NSGA-III
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