A guide to expert systems
Programming in Prolog
A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
What size net gives valid generalization?
Neural Computation
Optimizing neural networks using faster, more accurate genetic search
Proceedings of the third international conference on Genetic algorithms
Deduction in top-down inductive learning
Proceedings of the sixth international workshop on Machine learning
Finding new rules for incomplete theories: explicit biases for induction with contextual information
Proceedings of the sixth international workshop on Machine learning
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Designing application-specific neural networks using the genetic algorithm
Advances in neural information processing systems 2
Advances in neural information processing systems 2
Constructing hidden units using examples and queries
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
The Utility of Knowledge in Inductive Learning
Machine Learning
Compiling prior knowledge into an explicit basis
ML92 Proceedings of the ninth international workshop on Machine learning
An approach to anytime learning
ML92 Proceedings of the ninth international workshop on Machine learning
Training second-order recurrent neural networks using hints
ML92 Proceedings of the ninth international workshop on Machine learning
Maintaining the utility of learned knowledge using model-based adaptive control
Maintaining the utility of learned knowledge using model-based adaptive control
Symbolic knowledge and neural networks: insertion, refinement and extraction
Symbolic knowledge and neural networks: insertion, refinement and extraction
A practical Bayesian framework for backpropagation networks
Neural Computation
Machine Learning - Special issue on multistrategy learning
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
Knowledge-based artificial neural networks
Artificial Intelligence
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Inductive Policy: The Pragmatics of Bias Selection
Machine Learning - Special issue on bias evaluation and selection
Adaptive global optimization with local search
Adaptive global optimization with local search
An introduction to genetic algorithms
An introduction to genetic algorithms
Exploring the Power of Genetic Search in Learning Symbolic Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive individuals in evolving populations: models and algorithms
Adaptive individuals in evolving populations: models and algorithms
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Machine Learning
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Lamarckian Evolution, The Baldwin Effect and Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Using Prior Knowledge in a {NNPDA} to Learn Context-Free Languages
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Combining symbolic and connectionist learning methods to refine certainty-factor rule-bases
Combining symbolic and connectionist learning methods to refine certainty-factor rule-bases
Refining pid controllers using neural networks
Neural Computation
Journal of Artificial Intelligence Research
Rerepresenting and restructuring domain theories: a constructive induction approach
Journal of Artificial Intelligence Research
Hybrid learning using genetic algorithms and decision trees for pattern classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Automated Refinement of a Requirements Domain Theory
Automated Software Engineering
Evolving neural networks through augmenting topologies
Evolutionary Computation
Data-Driven Theory Refinement Using KBDistAl
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Mining biomolecular data using background knowledge and artificial neural networks
Handbook of massive data sets
New voting strategies designed for the classification of nucleic sequences
Knowledge and Information Systems
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Diagnostic Knowledge Acquisition for Agent-Based Medical Applications
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Information Sciences: an International Journal
Real-time evolution of neural networks in the NERO video game
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Interactive evolution of particle systems for computer graphics and animation
IEEE Transactions on Evolutionary Computation
Proceedings of the 8th International Conference on Frontiers of Information Technology
Computational intelligence in bioinformatics
Transactions on Rough Sets III
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An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the Regent algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.