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» Short Term Unit-Commitment Using Genetic Algorithms
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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
12 years 11 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
GECCO
2006
Springer
153views Optimization» more  GECCO 2006»
13 years 11 months ago
Analysis of the difficulty of learning goal-scoring behaviour for robot soccer
Learning goal-scoring behaviour from scratch for simulated robot soccer is considered to be a very difficult problem, and is often achieved by endowing players with an innate set ...
Jeff Riley, Victor Ciesielski
WABI
2007
Springer
110views Bioinformatics» more  WABI 2007»
14 years 2 months ago
Haplotype Inference Via Hierarchical Genotype Parsing
The within-species genetic variation due to recombinations leads to a mosaic-like structure of DNA. This structure can be modeled, e.g. by parsing sample sequences of current DNA w...
Pasi Rastas, Esko Ukkonen
GECCO
2006
Springer
220views Optimization» more  GECCO 2006»
13 years 11 months ago
Comparing evolutionary algorithms on the problem of network inference
In this paper, we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of different evoluti...
Christian Spieth, Rene Worzischek, Felix Streicher...
GECCO
2005
Springer
157views Optimization» more  GECCO 2005»
14 years 1 months ago
Simple addition of ranking method for constrained optimization in evolutionary algorithms
During the optimization of a constrained problem using evolutionary algorithms (EAs), an individual in the population can be described using three important properties, i.e., obje...
Pei Yee Ho, Kazuyuki Shimizu