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EVOW
2009
Springer
14 years 4 months ago
Evolutionary Optimization Guided by Entropy-Based Discretization
The Learnable Evolution Model (LEM) involves alternating periods of optimization and learning, performa extremely well on a range of problems, a specialises in achieveing good resu...
Guleng Sheri, David W. Corne
GECCO
2009
Springer
125views Optimization» more  GECCO 2009»
14 years 1 months ago
Preserving population diversity for the multi-objective vehicle routing problem with time windows
The Vehicle Routing Problem’s main objective is to find the lowest-cost set of routes to deliver goods to customers, which have a service time window, using a fleet of identic...
Abel Garcia-Najera
GECCO
2005
Springer
129views Optimization» more  GECCO 2005»
14 years 2 months ago
Search space modulation in genetic algorithms: evolving the search space by sinusoidal transformations
An experimental form of Modulation (Reinterpretation) of the Search Space is presented. This modulation is developed as a mathematical method that can be implemented directly into...
José Antonio Martin H.
EMO
2005
Springer
68views Optimization» more  EMO 2005»
14 years 2 months ago
Multi-objective Optimization of Problems with Epistemic Uncertainty
Abstract. Multi-objective evolutionary algorithms (MOEAs) have proven to be a powerful tool for global optimization purposes of deterministic problem functions. Yet, in many real-w...
Philipp Limbourg
AMC
2007
154views more  AMC 2007»
13 years 9 months ago
A hybrid particle swarm optimization-back-propagation algorithm for feedforward neural network training
The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become ve...
Jing-Ru Zhang, Jun Zhang, Tat-Ming Lok, Michael R....