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GECCO
2008
Springer
161views Optimization» more  GECCO 2008»
13 years 9 months ago
Neuro-evolution for a gathering and collective construction task
In this paper we apply three Neuro-Evolution (NE) methods as controller design approaches in a collective behavior task. These NE methods are Enforced Sub-Populations, MultiAgent ...
D. W. F. van Krevelen, Geoff S. Nitschke
GECCO
2008
Springer
363views Optimization» more  GECCO 2008»
13 years 9 months ago
Towards high speed multiobjective evolutionary optimizers
One of the major difficulties when applying Multiobjective Evolutionary Algorithms (MOEA) to real world problems is the large number of objective function evaluations. Approximate...
A. K. M. Khaled Ahsan Talukder
GECCO
2008
Springer
155views Optimization» more  GECCO 2008»
13 years 9 months ago
Towards memoryless model building
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical prope...
David Iclanzan, Dumitru Dumitrescu
ASC
2010
13 years 8 months ago
Simplifying Particle Swarm Optimization
The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs ...
M. E. H. Pedersen, Andrew J. Chipperfield
AR
2007
105views more  AR 2007»
13 years 8 months ago
Reinforcement learning of a continuous motor sequence with hidden states
—Reinforcement learning is the scheme for unsupervised learning in which robots are expected to acquire behavior skills through self-explorations based on reward signals. There a...
Hiroaki Arie, Tetsuya Ogata, Jun Tani, Shigeki Sug...