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GECCO
2006
Springer
188views Optimization» more  GECCO 2006»
15 years 1 months ago
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
This work describes a forward-looking approach for the solution of dynamic (time-changing) problems using evolutionary algorithms. The main idea of the proposed method is to combi...
Iason Hatzakis, David Wallace
GECCO
2008
Springer
107views Optimization» more  GECCO 2008»
14 years 10 months ago
The influence of scaling and assortativity on takeover times in scale-free topologies
In evolving systems, the topological characteristics of population structure have a pronounced impact on the rate of spread of advantageous alleles, and therefore affect selective...
Joshua L. Payne, Margaret J. Eppstein
70
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GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
15 years 3 months ago
An artificial immune network for multimodal function optimization on dynamic environments
Multimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the sea...
Fabrício Olivetti de França, Fernand...
CDC
2009
IEEE
217views Control Systems» more  CDC 2009»
14 years 10 months ago
Discrete invasive weed optimization algorithm: application to cooperative multiple task assignment of UAVs
This paper presents a novel discrete population based stochastic optimization algorithm inspired from weed colonization. Its performance in a discrete benchmark, timecost trade-off...
Mohsen Ramezani Ghalenoei, Hossein Hajimirsadeghi,...
AAAI
1998
14 years 11 months ago
Optimal 2D Model Matching Using a Messy Genetic Algorithm
A Messy Genetic Algorithm is customized toflnd'optimal many-to-many matches for 2D line segment models. The Messy GA is a variant upon the Standard Genetic Algorithm in which...
J. Ross Beveridge