Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Floating search methods in feature selection
Pattern Recognition Letters
Evolving neural networks through augmenting topologies
Evolutionary Computation
A Practical Approach to Feature Selection
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Automatic feature selection in neuroevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolving the mapping between input neurons and multi-source imagery
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
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
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Automatic feature selection in neuroevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolving a real-world vehicle warning system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Using group selection to evolve leadership in populations of self-replicating digital organisms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving a dynamic predictive coding mechanism for novelty detection
Knowledge-Based Systems
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 17th international conference on Parallel architectures and compilation techniques
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Machine learning in digital games: a survey
Artificial Intelligence Review
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Machine learning for event selection in high energy physics
Engineering Applications of Artificial Intelligence
Evolving multi-modal behavior in NPCs
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Evolving agent behavior in multiobjective domains using fitness-based shaping
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Adaptive navigation for autonomous robots
Robotics and Autonomous Systems
Sample aware embedded feature selection for reinforcement learning
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Artificial Intelligence in Medicine
Hi-index | 0.01 |
Feature selection is the process of finding the set of inputs to a machine learning algorithm that will yield the best performance. Developing a way to solve this problem automatically would make current machine learning methods much more useful. Previous efforts to automate feature selection rely on expensive meta-learning or are applicable only when labeled training data is available. This paper presents a novel method called FS-NEAT which extends the NEAT neuroevolution method to automatically determine an appropriate set of inputs for the networks it evolves. By learning the network's inputs, topology, and weights simultaneously, FS-NEAT addresses the feature selection problem without relying on meta-learning or labeled data. Initial experiments in an autonomous car racing simulation demonstrate that FS-NEAT can learn better and faster than regular NEAT. In addition, the networks it evolves are smaller and require fewer inputs. Furthermore, FS-NEAT's performance remains robust even as the feature selection task it faces is made increasingly difficult.