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Everything about Hypervolume indicator
Quality indicator for Pareto-front approximation sets
The hypervolume indicator is a performance indicator used in multi-objective optimization to measure the quality of a finite approximation of the Pareto frontier. It measures the Lebesgue measure of the region of objective space that is dominated by an approximation set and bounded by a specified reference point. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Hypervolume indicatorA 5-minute orientation
- Skim a recent paper: Differential Evolution with fitness-difference based parameter control and hypervolume diversity indicator for numerical optimizationInfluential work since 2022
- Pick up a classic book: FOGA '09The most-reprinted book on the subject
- Read the landmark paper: A Survey on the Hypervolume Indicator in Evolutionary Multiobjective OptimizationCited 401 times
- Go sideways: Evolutionary multi-objective optimizationA closely related topic
- Go sideways: Multi-objective optimizationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A Survey on the Hypervolume Indicator in Evolutionary Multiobjective Optimization
The Hypervolume Indicator
On the Complexity of Computing the Hypervolume Indicator
A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm
On using the hypervolume indicator to compare Pareto fronts: Applications to multi-criteria optimal experimental design
Approximation quality of the hypervolume indicator
A box decomposition algorithm to compute the hypervolume indicator
A novel hybrid hypervolume indicator and reference vector adaptation strategies based evolutionary algorithm for many-objective optimization
🆕 Influential since 2022
Differential Evolution with fitness-difference based parameter control and hypervolume diversity indicator for numerical optimization
Towards fast approximations for the hypervolume indicator for multi-objective optimization problems by Genetic Programming
A hypervolume approach to niche specialism, tested for the old-growth indicator status of calicioids
D-PHI: Desirability-Based Hypervolume Indicator for Interactive Multiobjective Optimization Using Aspiration and Reservation Levels as Preferences
📚 Books worth reading
- 📕FOGA '09Workshop on Foundations of Genetic Algorithms (10th 2009 Orlando, Fla.) · 2009
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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