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TEC
2010
77views more  TEC 2010»
8 years 8 months ago
On Set-Based Multiobjective Optimization
Abstract--Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: (i) how to fo...
Eckart Zitzler, Lothar Thiele, Johannes Bader
EUSFLAT
2009
156views Fuzzy Logic» more  EUSFLAT 2009»
8 years 11 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
ACMSE
2008
ACM
9 years 3 months ago
Training approaches in neural enhancement for multiobjective optimization
In previous work, a neural network was used to increase the number of solutions found by an evolutionary multiobjective optimization algorithm. In this paper, various approaches a...
Aaron Garrett, Gerry V. Dozier
EMO
2006
Springer
110views Optimization» more  EMO 2006»
9 years 5 months ago
Capabilities of EMOA to Detect and Preserve Equivalent Pareto Subsets
Recent works in evolutionary multiobjective optimization suggest to shift the focus from solely evaluating optimization success in the objective space to also taking the decision s...
Günter Rudolph, Boris Naujoks, Mike Preuss
GECCO
2004
Springer
244views Optimization» more  GECCO 2004»
9 years 6 months ago
Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer
In this paper, we present an extension of the heuristic called “particle swarm optimization” (PSO) that is able to deal with multiobjective optimization problems. Our approach ...
Gregorio Toscano Pulido, Carlos A. Coello Coello
GECCO
2005
Springer
116views Optimization» more  GECCO 2005»
9 years 7 months ago
An empirical study on the handling of overlapping solutions in evolutionary multiobjective optimization
We focus on the handling of overlapping solutions in evolutionary multiobjective optimization (EMO) algorithms. First we show that there exist a large number of overlapping soluti...
Hisao Ishibuchi, Kaname Narukawa, Yusuke Nojima
EMO
2005
Springer
126views Optimization» more  EMO 2005»
9 years 7 months ago
The Evolution of Optimality: De Novo Programming
Abstract. Evolutionary algorithms have been quite effective in dealing with single-objective “optimization” while the area of Evolutionary Multiobjective Optimization (EMOO) h...
Milan Zeleny
EMO
2005
Springer
194views Optimization» more  EMO 2005»
9 years 7 months ago
An EMO Algorithm Using the Hypervolume Measure as Selection Criterion
Abstract. The hypervolume measure is one of the most frequently applied measures for comparing the results of evolutionary multiobjective optimization algorithms (EMOA). The idea t...
Michael Emmerich, Nicola Beume, Boris Naujoks
GECCO
2007
Springer
163views Optimization» more  GECCO 2007»
9 years 7 months ago
Effects of the use of non-geometric binary crossover on evolutionary multiobjective optimization
In the design of evolutionary multiobjective optimization (EMO) algorithms, it is important to strike a balance between diversity and convergence. Traditional mask-based crossover...
Hisao Ishibuchi, Yusuke Nojima, Noritaka Tsukamoto...
GECCO
2007
Springer
209views Optimization» more  GECCO 2007»
9 years 7 months ago
Guided hyperplane evolutionary algorithm
A new evolutionary technique for multicriteria optimization called Guiding Hyper-plane Evolutionary Algorithm (GHEA) is proposed. The originality of the approach consists in the f...
Corina Rotar, D. Dumitrescu, Rodica Ioana Lung
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