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GECCO
2000
Springer
145views Optimization» more  GECCO 2000»
15 years 10 months ago
Enhancing the GA's Ability to Cope with Dynamic Environments
: The Shifting Balance Genetic Algorithm (SBGA) is a pluggable module for a GA (or any other Evolutionary Algorithm) based on a modification of Sewall Wright's shifting balanc...
Mark Wineberg, Franz Oppacher
CEC
2005
IEEE
15 years 8 months ago
Equilibrium selection by co-evolution for bargaining problems under incomplete information about time preferences
Abstract- The main purpose of this work is to measure the effect of bargaining players’ information completeness on agreements in evolutionary environments. We apply Co-evolution...
Nanlin Jin
CP
2005
Springer
15 years 8 months ago
Evolving Variable-Ordering Heuristics for Constrained Optimisation
In this paper we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAX-SAT optimisation problems. Our approach of...
Stuart Bain, John Thornton, Abdul Sattar
GEM
2008
15 years 8 months ago
Initial Population Diversity Does Not Influence Performance
- It is widely believed that greater initial population diversity leads to improved performance in genetic algorithms. However, this assumption has not been rigorously tested previ...
Pedro A. Diaz-Gomez, Dean F. Hougen
CEC
2009
IEEE
16 years 1 months ago
The Pareto-Following Variation Operator as an alternative approximation model
— This paper presents a critical analysis of the Pareto-Following Variation Operator (PFVO) when used as an approximation method for Multiobjective Evolutionary Algorithms (MOEA)...
A. K. M. Khaled Ahsan Talukder, Michael Kirley, Ra...