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GECCO
2008
Springer
163views Optimization» more  GECCO 2008»
13 years 9 months ago
Embedded evolutionary multi-objective optimization for worst case robustness
In Multi-Objective Problems (MOPs) involving uncertainty, each solution might be associated with a cluster of performances in the objective space depending on the possible scenari...
Gideon Avigad, Jürgen Branke
CEC
2009
IEEE
14 years 2 months ago
Local search based evolutionary multi-objective optimization algorithm for constrained and unconstrained problems
Abstract— Evolutionary multi-objective optimization algorithms are commonly used to obtain a set of non-dominated solutions for over a decade. Recently, a lot of emphasis have be...
Karthik Sindhya, Ankur Sinha, Kalyanmoy Deb, Kaisa...
ASC
2004
13 years 7 months ago
Solving nonconvex climate control problems: pitfalls and algorithm performances
Global optimization can be used as the main component for reliable decision support systems. In this contribution, we explore numerical solution techniques for nonconvex and nondi...
Carmen G. Moles, Julio R. Banga, Klaus Keller
PPSN
2010
Springer
13 years 6 months ago
Design and Comparison of two Evolutionary Approaches for Solving the Rubik's Cube
Solutions calculated by Evolutionary Algorithms have come to surpass exact methods for solving various problems. The Rubik’s Cube multiobjective optimization problem is one such ...
Nail El-Sourani, Markus Borschbach
GECCO
2007
Springer
182views Optimization» more  GECCO 2007»
14 years 2 months ago
An analysis of the effects of population structure on scalable multiobjective optimization problems
Multiobjective evolutionary algorithms (MOEA) are an effective tool for solving search and optimization problems containing several incommensurable and possibly conflicting objec...
Michael Kirley, Robert L. Stewart