Open AccessJournal Article
The micro genetic algorithm 2: Towards online adaptation in evolutionary multiobjective optimization
TLDR
In this paper, a revised version of the micro-GA for multi-objective optimization is proposed, which does not require any parameter fine-tuning and can be used for online adaptation.Abstract:
In this paper, we deal with an important issue generally omitted in the current literature on evolutionary multiobjective optimization: on-line adaptation. We propose a revised version of our micro-GA for multiobjective optimization which does not require any parameter fine-tuning. Furthermore, we introduce in this paper a dynamic selection scheme through which our algorithm decides which is the best crossover operator to be used at any given time. Such a scheme has helped to improve the performance of the new version of the algorithm which is called the micro-GA2 (μGA 2 ), The new approach is validated using several test function and metrics taken from the specialized literature and it is compared to the NSGA-Il and PAES.read more
Citations
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Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored
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Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index.
Antonio Oseas de Carvalho Filho,Wener Borges de Sampaio,Aristófanes Corrêa Silva,Anselmo Cardoso de Paiva,Rodolfo Acatauassú Nunes,Marcelo Gattass +5 more
TL;DR: Lung cancer presents the highest mortality rate in addition to one of the smallest survival rates after diagnosis, an early diagnosis considerably increases the survival chance of patients, and the methodology proposed herein contributes to this diagnosis by being a useful tool for specialists who are attempting to detect nodules.
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.
Book
Self-Organizing Maps
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
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.
Journal ArticleDOI
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
TL;DR: The problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front are studied to enable researchers to test their algorithms for specific aspects of multi- objective optimization.
Proceedings ArticleDOI
Scalable multi-objective optimization test problems
TL;DR: Three different approaches for systematically designing test problems for systematic designing multi-objective evolutionary algorithms (MOEAs) showing efficacy in handling problems having more than two objectives are suggested.
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