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GECCO
2009
Springer
199views Optimization» more  GECCO 2009»
15 years 4 months ago
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Encouraging exploration, typically by preserving the diversity within the population, is one of the most common method to improve the behavior of evolutionary algorithms with dece...
Jean-Baptiste Mouret, Stéphane Doncieux
NIPS
2003
15 years 1 months ago
Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks
Gradient-following learning methods can encounter problems of implementation in many applications, and stochastic variants are frequently used to overcome these difficulties. We ...
Justin Werfel, Xiaohui Xie, H. Sebastian Seung
SGAI
2007
Springer
15 years 5 months ago
Learning Sets of Sub-Models for Spatio-Temporal Prediction
In this paper we describe a novel technique which implements a spatiotemporal model as a set of sub-models based on first order logic. These sub-models model different, typicall...
Andrew Bennett, Derek R. Magee
ICIC
2009
Springer
14 years 9 months ago
Inference of Differential Equation Models by Multi Expression Programming for Gene Regulatory Networks
This paper presents an evolutionary method for identifying the gene regulatory network from the observed time series data of gene expression using a system of ordinary differential...
Bin Yang, Yuehui Chen, Qingfang Meng
EUROGP
1999
Springer
137views Optimization» more  EUROGP 1999»
15 years 4 months ago
Evolving Controllers for Autonomous Agents Using Genetically Programmed Networks
– This article presents a new approach to the evolution of controllers for autonomous agents. We propose the evolution of a connectionist structure where each node has an associa...
Arlindo Silva, Ana Neves, Ernesto Costa