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ICAI
2003
14 years 11 months ago
Exploiting the Marginal Profits of Constraints with Evolutionary Multi-Objective Optimization Techniques
Many real-world search and optimization problems naturally involve constraint handling. Recently, quite a few heuristic methods were proposed to solve the nonlinear constrained op...
Zhenyu Yan, Wei Zhi, Lishan Kang
110
Voted
GECCO
2007
Springer
190views Optimization» more  GECCO 2007»
15 years 4 months ago
Interactive evolutionary multi-objective optimization and decision-making using reference direction method
In this paper, we borrow the concept of reference direction approach from the multi-criterion decision-making literature and combine it with an EMO procedure to develop an algorit...
Kalyanmoy Deb, Abhishek Kumar
CEC
2011
IEEE
13 years 10 months ago
Inferring transcriptional regulators for sets of co-expressed genes by multi-objective evolutionary optimization
Abstract—Higher organisms are able to respond to continuously changing external conditions by transducing cellular signals into specific regulatory programs, which control gene ...
Adrian Schröder, Clemens Wrzodek, Johannes Wo...
91
Voted
EMO
2006
Springer
124views Optimization» more  EMO 2006»
15 years 1 months ago
I-MODE: An Interactive Multi-objective Optimization and Decision-Making Using Evolutionary Methods
With the popularity of efficient multi-objective evolutionary optimization (EMO) techniques and the need for such problem-solving activities in practice, EMO methodologies and EMO ...
Kalyanmoy Deb, Shamik Chaudhuri
EUSFLAT
2009
156views Fuzzy Logic» more  EUSFLAT 2009»
14 years 8 months ago
Interactive Fuzzy Modeling by Evolutionary Multiobjective Optimization with User Preference
One of the new trends in genetic fuzzy systems (GFS) is the use of evolutionary multiobjective optimization (EMO) algorithms. This is because EMO algorithms can easily handle two c...
Yusuke Nojima, Hisao Ishibuchi