190 search results - page 9 / 38 » Combining Convergence and Diversity in Evolutionary Multiobj... |
125
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GECCO
2003
Springer
15 years 7 months ago
2003
Springer
One advantage of evolutionary multiobjective optimization (EMO) algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their si...
106
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EMO
2005
Springer
15 years 7 months ago
2005
Springer
Nearly all Multi-Objective Evolutionary Algorithms (MOEA) rely on random generation of initial population. In large and complex search spaces, this random method often leads to an ...
CEC
2007
IEEE
15 years 8 months ago
2007
IEEE
—In recent years a lot of progress has been made in understanding the behavior of evolutionary computation methods for single- and multi-objective problems. Our aim is to analyze...
116
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ENC
2003
IEEE
15 years 7 months ago
2003
IEEE
This paper presents the main multiobjective optimization concepts that have been used in evolutionary algorithms to handle constraints in global optimization problems. A review of...
121
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GECCO
2005
Springer
15 years 7 months ago
2005
Springer
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...