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1999
Springer
14 years 2 months ago
Dynamic Demes parallel genetic algorithm
Abstract—Dynamic Demes is a new method for the parallelisation of evolutionary algorithms. It was derived as a combination of two other parallelisation algorithms: the master-sla...
Mariusz Nowostawski, Riccardo Poli
GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
14 years 1 months ago
Multi-step environment learning classifier systems applied to hyper-heuristics
Heuristic Algorithms (HA) are very widely used to tackle practical problems in operations research. They are simple, easy to understand and inspire confidence. Many of these HAs a...
Javier G. Marín-Blázquez, Sonia Schu...
CEC
2010
IEEE
13 years 11 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
EC
2008
174views ECommerce» more  EC 2008»
13 years 9 months ago
Automated Design of Image Operators that Detect Interest Points
This work describes how evolutionary computation can be used to synthesize lowlevel image operators that detect interesting points on digital images. Interest point detection is a...
Leonardo Trujillo, Gustavo Olague
CEC
2011
IEEE
12 years 9 months ago
Cost-benefit analysis of using heuristics in ACGP
—Constrained Genetic Programming (CGP) is a method of searching the Genetic Programming search space non-uniformly, giving preferences to certain subspaces according to some heur...
John W. Aleshunas, Cezary Z. Janikow