Book ChapterDOI
A Variant of Evolution Strategies for Vector Optimization
Frank Kursawe
- pp 193-197
About:
This article is published in Parallel Problem Solving from Nature.The article was published on 1990-10-01. It has received 650 citations till now. The article focuses on the topics: Vector optimization & Multi-swarm optimization.read more
Citations
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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 ArticleDOI
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Eckart Zitzler,Lothar Thiele +1 more
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Book
Evolutionary algorithms for solving multi-objective problems
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
SPEA2: Improving the strength pareto evolutionary algorithm
TL;DR: An improved version of SPEA, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method.
Journal ArticleDOI
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
References
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Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Book
Multiple Attribute Decision Making: Methods and Applications
TL;DR: In this paper, the authors present a classification of MADM methods by data type and propose a ranking method based on the degree of similarity of the MADM method to the original MADM algorithm.
Book
Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution
Book
Multiple Objective Decision Making ― Methods and Applications: A State-of-the-Art Survey
Ching-Lai Hwang,Kwangsun Yoon +1 more
TL;DR: On MADM Methods Classification.
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Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Muiltiobjective optimization using nondominated sorting in genetic algorithms
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