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EC
2000
96views ECommerce» more  EC 2000»
13 years 8 months ago
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied ...
David A. van Veldhuizen, Gary B. Lamont
GECCO
2005
Springer
131views Optimization» more  GECCO 2005»
14 years 2 months ago
Evolutionary optimization of dynamic control problems accelerated by progressive step reduction
In this paper, we describe the use of an evolutionary algorithm (EA) to solve dynamic control optimization problems in engineering. In this class of problems, a set of control var...
Q. Tuan Pham
GECCO
2007
Springer
163views Optimization» more  GECCO 2007»
14 years 2 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
2009
Springer
14 years 3 months ago
An evolutionary approach to planning IEEE 802.16 networks
Efficient and effective deployment of IEEE 802.16 networks to service an area of users with certain traffic demands is an important network planning problem. We resort to an evol...
Ting Hu, Yuanzhu Peter Chen, Wolfgang Banzhaf, Rob...
GECCO
2008
Springer
156views Optimization» more  GECCO 2008»
13 years 9 months ago
Computing minimum cuts by randomized search heuristics
We study the minimum s-t-cut problem in graphs with costs on the edges in the context of evolutionary algorithms. Minimum cut problems belong to the class of basic network optimiz...
Frank Neumann, Joachim Reichel, Martin Skutella