Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Multilayer feedforward networks are universal approximators
Neural Networks
A resource-allocating network for function interpolation
Neural Computation
Universal approximation using radial-basis-function networks
Neural Computation
Genetic Reinforcement Learning for Neurocontrol Problems
Machine Learning - Special issue on genetic algorithms
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Hierarchical learning with procedural abstraction mechanisms
Hierarchical learning with procedural abstraction mechanisms
A Theory of Program Size Formally Identical to Information Theory
Journal of the ACM (JACM)
An overview of radial basis function networks
Radial basis function networks 2
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Radial Basis Function Method for Global Optimization
Journal of Global Optimization
Classifiers that approximate functions
Natural Computing: an international journal
Evolving Neural Control Systems
IEEE Expert: Intelligent Systems and Their Applications
Evolving neural networks through augmenting topologies
Evolutionary Computation
Combining Competitive And Cooperative Coevolution For Training Cascade Neural Networks
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Accuracy-based Neuro And Neuro-fuzzy Classifier Systems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Solving Non-Markovian Control Tasks with Neuro-Evolution
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Design of structural modular neural networks with genetic algorithm
Advances in Engineering Software
Evolving Soccer Keepaway Players Through Task Decomposition
Machine Learning
Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
XCS with computed prediction in multistep environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Neuroevolution of an automobile crash warning system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier prediction based on tile coding
Proceedings of the 8th 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
Fast learning in networks of locally-tuned processing units
Neural Computation
Context-dependent predictions and cognitive arm control with XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Self-adaptive constructivism in Neural XCS and XCSF
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
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
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Model discrimination using an algorithmic information criterion
Automatica (Journal of IFAC)
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
On global-local artificial neural networks for function approximation
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Evolving multi-modal behavior in NPCs
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Autonomous Agents and Multi-Agent Systems
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Critical factors in the performance of hyperNEAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Generative and developmental systems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Evolution of neural networks, or neuroevolution, has been a successful approach to many low-level control problems such as pole balancing, vehicle control, and collision warning. However, certain types of problems-such as those involving strategic decision-making-have remained difficult for neuroevolution to solve. This paper evaluates the hypothesis that such problems are difficult because they are fractured: The correct action varies discontinuously as the agent moves from state to state. A method for measuring fracture using the concept of function variation is proposed and, based on this concept, two methods for dealing with fracture are examined: neurons with local receptive fields, and refinement based on a cascaded network architecture. Experiments in several benchmark domains are performed to evaluate how different levels of fracture affect the performance of neuroevolution methods, demonstrating that these two modifications improve performance significantly. These results form a promising starting point for expanding neuroevolution to strategic tasks.