SPEA2: Improving the strength pareto evolutionary algorithm
- E. Zitzler, M. Laumanns, L. Thiele
- Computer Science
- 2001
An improved version of SPEA, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method.
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
- E. Zitzler, L. Thiele
- Computer ScienceIEEE Transactions on Evolutionary Computation
- 1 November 1999
The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
- E. Zitzler, K. Deb, L. Thiele
- Computer ScienceEvolutionary Computation
- 1 June 2000
This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
Performance assessment of multiobjective optimizers: an analysis and review
- E. Zitzler, L. Thiele, M. Laumanns, C. Fonseca, V. G. D. Fonseca
- Computer ScienceIEEE Transactions on Evolutionary Computation
- 1 April 2003
This study provides a rigorous analysis of the limitations underlying this type of quality assessment in multiobjective evolutionary algorithms and develops a mathematical framework which allows one to classify and discuss existing techniques.
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
- E. Zitzler, M. Laumanns, L. Thiele
- Computer Science
- 2002
Scalable Test Problems for Evolutionary Multiobjective Optimization
- K. Deb, L. Thiele, M. Laumanns, E. Zitzler
- Computer ScienceEvolutionary Multiobjective Optimization
- 2005
Three different approaches for systematically designing test problems for systematically demonstrating the efficacy of multiobjective evolutionary algorithms in handling problems having more than two objectives are suggested.
Scalable multi-objective optimization test problems
- K. Deb, L. Thiele, M. Laumanns, E. Zitzler
- Computer ScienceProceedings of the Congress on Evolutionary…
- 12 May 2002
Three different approaches for systematically designing test problems for systematic designing multi-objective evolutionary algorithms (MOEAs) showing efficacy in handling problems having more than two objectives are suggested.
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
- E. Zitzler, L. Thiele
- Computer ScienceParallel Problem Solving from Nature
- 27 September 1998
In this paper an extensive, quantitative comparison is presented, applying four multiobjective evolutionary algorithms to an extended 0/1 knapsack problem.
A systematic comparison and evaluation of biclustering methods for gene expression data
- A. Prelic, S. Bleuler, E. Zitzler
- Computer ScienceBioinform.
- 1 May 2006
A methodology for comparing and validating biclustering methods that includes a simple binary reference model that captures the essential features of most bic Lustering approaches and proposes a fast divide-and-conquer algorithm (Bimax).
Efficient network flooding and time synchronization with Glossy
- F. Ferrari, Marco Zimmerling, L. Thiele, O. Saukh
- Computer ScienceProceedings of the 10th ACM/IEEE International…
- 12 April 2011
This paper presents Glossy, a novel flooding architecture for wireless sensor networks. Glossy exploits constructive interference of IEEE 802.15.4 symbols for fast network flooding and implicit time…
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