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GECCO
2000
Springer
145views Optimization» more  GECCO 2000»
15 years 6 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 4 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
99
Voted
GEM
2008
15 years 3 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
134
Voted
CEC
2008
IEEE
15 years 9 months ago
Path optimization for multiple objectives in directed graphs using genetic algorithms
Abstract— This paper presents a genetic algorithmic approach for finding efficient paths in directed graphs when optimizing multiple objectives. Its aim is to provide solutions...
Juan Rada, Ruben Parma, Wilmer Pereira
CEC
2010
IEEE
15 years 3 months ago
Many-objective Distinct Candidates Optimization using Differential Evolution on centrifugal pump design problems
Many-objective problems are difficult to solve using conventional multi-objective evolutionary algorithms (MOEAs) as these algorithms rely primarily on Pareto ranking to guide the ...
Peter Dueholm Justesen, Rasmus K. Ursem