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Multiobjective evolutionary algorithms: A survey of the state of the art
A Zhou, BY Qu, H Li, SZ Zhao, PN Suganthan… - Swarm and evolutionary …, 2011 - Elsevier
A multiobjective optimization problem involves several conflicting objectives and has a set of
Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …
Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …
A review of the application of multiobjective evolutionary fuzzy systems: Current status and further directions
M Fazzolari, R Alcala, Y Nojima… - … on Fuzzy systems, 2012 - ieeexplore.ieee.org
Over the past few decades, fuzzy systems have been widely used in several application
fields, thanks to their ability to model complex systems. The design of fuzzy systems has …
fields, thanks to their ability to model complex systems. The design of fuzzy systems has …
A survey of evolutionary computation for association rule mining
A Telikani, AH Gandomi, A Shahbahrami - Information Sciences, 2020 - Elsevier
Abstract Association Rule Mining (ARM) is a significant task for discovering frequent patterns
in data mining. It has achieved great success in a plethora of applications such as market …
in data mining. It has achieved great success in a plethora of applications such as market …
Multiobjective evolutionary algorithms for electric power dispatch problem
MA Abido - IEEE transactions on evolutionary computation, 2006 - ieeexplore.ieee.org
The potential and effectiveness of the newly developed Pareto-based multiobjective
evolutionary algorithms (MOEA) for solving a real-world power system multiobjective …
evolutionary algorithms (MOEA) for solving a real-world power system multiobjective …
Genetic fuzzy systems: taxonomy, current research trends and prospects
F Herrera - Evolutionary Intelligence, 2008 - Springer
The use of genetic algorithms for designing fuzzy systems provides them with the learning
and adaptation capabilities and is called genetic fuzzy systems (GFSs). This topic has …
and adaptation capabilities and is called genetic fuzzy systems (GFSs). This topic has …
Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms
J Alcalá-Fdez, R Alcalá, MJ Gacto, F Herrera - Fuzzy Sets and Systems, 2009 - Elsevier
Different studies have proposed methods for mining fuzzy association rules from quantitative
data, where the membership functions were assumed to be known in advance. However, it …
data, where the membership functions were assumed to be known in advance. However, it …
A survey on association rules mining using heuristics
SM Ghafari, C Tjortjis - Wiley Interdisciplinary Reviews: Data …, 2019 - Wiley Online Library
Association rule mining (ARM) is a commonly encountred data mining method. There are
many approaches to mining frequent rules and patterns from a database and one among …
many approaches to mining frequent rules and patterns from a database and one among …
NMEEF-SD: Non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup discovery
CJ Carmona, P González… - IEEE Transactions on …, 2010 - ieeexplore.ieee.org
A non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup
discovery (NMEEF-SD) is described and analyzed in this paper. This algorithm, which is …
discovery (NMEEF-SD) is described and analyzed in this paper. This algorithm, which is …
A constraint handling technique for constrained multi-objective genetic algorithm
Q Long - Swarm and Evolutionary Computation, 2014 - Elsevier
A new constraint handling technique for multi-objective genetic algorithm is proposed in this
paper. There are two important issues in multi-objective genetic algorithm, closeness of the …
paper. There are two important issues in multi-objective genetic algorithm, closeness of the …
Metaheuristics for data mining: survey and opportunities for big data
C Dhaenens, L Jourdan - Annals of operations research, 2022 - Springer
In the context of big data, many scientific communities aim to provide efficient approaches to
accommodate large-scale datasets. This is the case of the machine-learning community …
accommodate large-scale datasets. This is the case of the machine-learning community …