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GECCO
2003
Springer
112views Optimization» more  GECCO 2003»
15 years 3 months ago
Dispersion-Based Population Initialization
Reliable execution and analysis of an evolutionary algorithm (EA) normally requires many runs to provide reasonable assurance that stochastic effects have been properly considered...
Ronald W. Morrison
GECCO
2004
Springer
155views Optimization» more  GECCO 2004»
15 years 3 months ago
Genetic Network Programming with Reinforcement Learning and Its Performance Evaluation
A new graph-based evolutionary algorithm named “Genetic Network Programming, GNP” has been proposed. GNP represents its solutions as directed graph structures, which can improv...
Shingo Mabu, Kotaro Hirasawa, Jinglu Hu
116
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GECCO
2010
Springer
244views Optimization» more  GECCO 2010»
14 years 10 months ago
Implicit fitness and heterogeneous preferences in the genetic algorithm
This paper takes an economic approach to derive an evolutionary learning model based entirely on the endogenous employment of genetic operators in the service of self-interested a...
Justin T. H. Smith
GECCO
2008
Springer
175views Optimization» more  GECCO 2008»
14 years 11 months ago
Non-genetic transmission of memes by diffusion
In recent years, there has been an increase in research activities on Memetic Algorithm (MA). MA works with memes; a meme being defined as “the basic unit of cultural transmiss...
Quang Huy Nguyen 0001, Yew-Soon Ong, Meng-Hiot Lim
GECCO
2009
Springer
142views Optimization» more  GECCO 2009»
15 years 5 months ago
A stopping criterion based on Kalman estimation techniques with several progress indicators
The need for a stopping criterion in MOEA’s is a repeatedly mentioned matter in the domain of MOOP’s, even though it is usually left aside as secondary, while stopping criteri...
José Luis Guerrero, Jesús Garc&iacut...