Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Genetic Reinforcement Learning for Neurocontrol Problems
Machine Learning - Special issue on genetic algorithms
Floating search methods in feature selection
Pattern Recognition Letters
Neural network design
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
Evolving neural networks through augmenting topologies
Evolutionary Computation
An introduction to variable and feature selection
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Automatic feature selection in neuroevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Algorithms for Feature Selection: An Evaluation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Pleural nodule identification in low-dose and thin-slice lung computed tomography
Computers in Biology and Medicine
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Analysing the evolvability of neural network agents through structural mutations
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
An analysis of early studies released by the lung imaging database consortium (LIDC)
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Sample aware embedded feature selection for reinforcement learning
Proceedings of the 14th annual conference on Genetic and evolutionary computation
On the Performance of Indirect Encoding Across the Continuum of Regularity
IEEE Transactions on Evolutionary Computation
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Objective: In the field of computer-aided detection (CAD) systems for lung nodules in computed tomography (CT) scans, many image features are presented and many artificial neural network (ANN) classifiers with various structural topologies are analyzed; frequently, the classifier topologies are selected by trial-and-error experiments. To avoid these trial and error approaches, we present a novel classifier that evolves ANNs using genetic algorithms, called ''Phased Searching with NEAT in a Time or Generation-Scaled Framework'', integrating feature selection with the classification task. Methods and materials: We analyzed our method's performance on 360 CT scans from the public Lung Image Database Consortium database. We compare our method's performance with other more-established classifiers, namely regular NEAT, Feature-Deselective NEAT (FD-NEAT), fixed-topology ANNs, and support vector machines (SVMs) using ten-fold cross-validation experiments of all 360 scans. Results: The results show that the proposed ''Phased Searching'' method performs better and faster than regular NEAT, better than FD-NEAT, and achieves sensitivities at 3 and 4 false positives (FP) per scan that are comparable with the fixed-topology ANN and SVM classifiers, but with fewer input features. It achieves a detection sensitivity of 83.0+/-9.7% with an average of 4FP/scan, for nodules with a diameter greater than or equal to 3mm. It also evolves networks with shorter evolution times and with lower complexities than regular NEAT (p=0.026 and p