Journal ArticleDOI
Interactive evolutionary multi-objective optimization for quasi-concave preference functions
TLDR
A new hybrid approach to interactive evolutionary multi-Objective optimization that uses a partial preference order to act as the fitness function in a customized genetic algorithm that demonstrates its performance on the multi-objective knapsack problem.About:
This article is published in European Journal of Operational Research.The article was published on 2010-10-16. It has received 68 citations till now. The article focuses on the topics: Interactive evolutionary computation & Evolutionary algorithm.read more
Citations
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Journal ArticleDOI
An Interactive Evolutionary Multiobjective Optimization Method Based on Progressively Approximated Value Functions
TL;DR: Results on two- to five-objective optimization problems using the progressively interactive NSGA-II approach show the simplicity of the proposed approach and its future promise.
Proceedings ArticleDOI
A review of hybrid evolutionary multiple criteria decision making methods
TL;DR: This paper reviews techniques which have combined evolutionary multi-objective optimization and multiple criteria decision making, including methods used to model the decision-makers preferences and example algorithms for each category.
Journal ArticleDOI
Evolutionary algorithms with preference polyhedron for interval multi-objective optimization problems
Dunwei Gong,Jing Sun,Xinfang Ji +2 more
TL;DR: A novel evolutionary algorithm that interacts with a decision maker during the optimization process to obtain the most preferred solution and the numerical results indicated that the method is simpler and more efficient than the a posteriori method.
Book ChapterDOI
Chapter Four – Preference Incorporation in Evolutionary Multiobjective Optimization: A Survey of the State-of-the-Art
TL;DR: A summary of the main preference-based MOEAs is provided together with a brief criticism that includes their pros and cons and a classification of such type of algorithms based on the DM's preference information structure is proposed.
Journal ArticleDOI
Using Choquet integral as preference model in interactive evolutionary multiobjective optimization
Juergen Branke,Salvatore Corrente,Salvatore Greco,Roman Słowiński,Roman Słowiński,Piotr Zielniewicz +5 more
TL;DR: An interactive multiobjective evolutionary algorithm that attempts to discover the most preferred part of the Pareto-optimal set is proposed and the Choquet integral is applied as a user’s preference model, allowing us to capture interactions between objectives.
References
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Journal ArticleDOI
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Journal Article
The magical number seven, plus or minus two: some limits on our capacity for processing information
TL;DR: The theory of information as discussed by the authors provides a yardstick for calibrating our stimulus materials and for measuring the performance of our subjects and provides a quantitative way of getting at some of these questions.
Book
The magical number seven plus or minus two: some limits on our capacity for processing information
TL;DR: The theory provides us with a yardstick for calibrating the authors' stimulus materials and for measuring the performance of their subjects, and the concepts and measures provided by the theory provide a quantitative way of getting at some of these questions.
Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Book
Response Surface Methodology: Process and Product Optimization Using Designed Experiments
TL;DR: Using a practical approach, this book discusses two-level factorial and fractional factorial designs, several aspects of empirical modeling with regression techniques, focusing on response surface methodology, mixture experiments and robust design techniques.