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GECCO
2008
Springer
148views Optimization» more  GECCO 2008»
15 years 2 months ago
Accelerating convergence using rough sets theory for multi-objective optimization problems
We propose the use of rough sets theory to improve the first approximation provided by a multi-objective evolutionary algorithm and retain the nondominated solutions using a new ...
Luis V. Santana-Quintero, Carlos A. Coello Coello
GECCO
2005
Springer
125views Optimization» more  GECCO 2005»
15 years 7 months ago
Improving EA-based design space exploration by utilizing symbolic feasibility tests
This paper will propose a novel approach in combining Evolutionary Algorithms with symbolic techniques in order to improve the convergence of the algorithm in the presence of larg...
Thomas Schlichter, Christian Haubelt, Jürgen ...
FOCS
2008
IEEE
15 years 8 months ago
Constant-Time Approximation Algorithms via Local Improvements
We present a technique for transforming classical approximation algorithms into constant-time algorithms that approximate the size of the optimal solution. Our technique is applic...
Huy N. Nguyen, Krzysztof Onak
GECCO
2006
Springer
130views Optimization» more  GECCO 2006»
15 years 5 months ago
An efficient multi-objective evolutionary algorithm with steady-state replacement model
The generic Multi-objective Evolutionary Algorithm (MOEA) aims to produce Pareto-front approximations with good convergence and diversity property. To achieve convergence, most mu...
Dipti Srinivasan, Lily Rachmawati
GECCO
2006
Springer
186views Optimization» more  GECCO 2006»
15 years 5 months ago
Comparison of multi-modal optimization algorithms based on evolutionary algorithms
Many engineering optimization tasks involve finding more than one optimum solution. The present study provides a comprehensive review of the existing work done in the field of mul...
Gulshan Singh, Kalyanmoy Deb