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» On the Brittleness of Evolutionary Algorithms
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CEC
2003
IEEE
15 years 11 months ago
Learning DFA: evolution versus evidence driven state merging
Learning Deterministic Finite Automata (DFA) is a hard task that has been much studied within machine learning and evolutionary computation research. This paper presents a new met...
Simon M. Lucas, T. Jeff Reynolds
GECCO
2009
Springer
125views Optimization» more  GECCO 2009»
15 years 11 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
CEC
2007
IEEE
15 years 10 months ago
Evolving hypernetworks for pattern classification
Abstract-- Hypernetworks consist of a large number of hyperedges that represent higher-order features sampled from training patterns. Evolutionary algorithms have been used as a me...
Joo-Kyung Kim, Byoung-Tak Zhang
CEC
2007
IEEE
15 years 10 months ago
Mobile robot global localization using differential evolution and particle swarm optimization
For a mobile robot to move in a known environment and operate successfully, first it needs to robustly determine its initial position and orientation relative to the map, and then ...
Ali R. Vahdat, Naser NourAshrafoddin, Saeed Shiry ...
GECCO
2006
Springer
202views Optimization» more  GECCO 2006»
15 years 10 months ago
Inference of genetic networks using S-system: information criteria for model selection
In this paper we present an evolutionary approach for inferring the structure and dynamics in gene circuits from observed expression kinetics. For representing the regulatory inte...
Nasimul Noman, Hitoshi Iba