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Punctuated equilibria: a parallel genetic algorithm
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Parallel genetic algorithms for a hypercube
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A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Using genetic algorithms to learn disjunctive rules from examples
Proceedings of the seventh international conference (1990) on Machine learning
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Deduction in top-down inductive learning
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Rigel: An Inductive Learning System
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Improved Estimates for the Accuracy of Small Disjuncts
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic programming: on the programming of computers by means of natural selection
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The Utility of Knowledge in Inductive Learning
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Learning structured concepts using genetic algorithms
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Multistrategy Learning and Theory Revision
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Using Genetic Algorithms for Concept Learning
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A Knowledge-Intensive Genetic Algorithm for Supervised Learning
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Competition-Based Induction of Decision Models from Examples
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Knowledge-based artificial neural networks
Artificial Intelligence
Learning Logical Definitions from Relations
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Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
Classifier Systems and the Animat Problem
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Explanation-Based Generalization: A Unifying View
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SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
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An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Distributed Genetic Algorithms
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Uniform Crossover in Genetic Algorithms
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Structure and Performance of Fine-Grain Parallelism in Genetic Search
Proceedings of the 5th International Conference on Genetic Algorithms
A Fine-Grained Parallel Genetic Algorithm for Distributed Parallel Systems
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A Coevolutionary Approach to Learning Sequential Decision Rules
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Analysis of Genetic Algorithms Evolution under Pure Selection
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IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Concept learning and the problem of small disjuncts
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SMART+: a multi-strategy learning tool
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Learning concept classification rules using genetic algorithms
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LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
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Learning classifier systems: a complete introduction, review, and roadmap
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A Study on the Effect of Cooperative Evolution on Concept Learning
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Resampling vs Reweighting in Boosting a Relational Weak Learner
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ART: A Hybrid Classification Model
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IEEE Transactions on Knowledge and Data Engineering
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AI Communications
An integrated approach for operational knowledge acquisition of refuse incinerators
Expert Systems with Applications: An International Journal
A parallel genetic algorithm approach for automated discovery of censored production rules
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
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Information Sciences: an International Journal
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An Evolutionary Ensemble-Based Method for Rule Extraction with Distributed Data
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Learning classifier systems: a complete introduction, review, and roadmap
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People detection through quantified fuzzy temporal rules
Pattern Recognition
A method for handling numerical attributes in GA-based inductive concept learners
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A genetic algorithms approach to ILP
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
ARO: A new model-free optimization algorithm inspired from asexual reproduction
Applied Soft Computing
Efficient Distributed Genetic Algorithm for Rule extraction
Applied Soft Computing
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Context-sensitive refinements for stochastic optimisation algorithms in inductive logic programming
Artificial Intelligence Review
A granular agent evolutionary algorithm for classification
Applied Soft Computing
Decision tree based learning and genetic based learning to detect network intrusions
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
Cooperative evolutive concept learning: an empirical study
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Evolutionary generation of prototypes for a learning vector quantization classifier
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Fundamenta Informaticae
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This paper describes REGAL, a distributed genetic algorithm-based system, designed for learning first-order logic concept descriptions from examples. The system is a hybrid of the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptions, each evolved separately. In order to increase effectiveness, REGAL is specifically tailored to the concept learning task; hence, REGAL is task-dependent, but, on the other hand, domain-independent. The system proved particularly robust with respect to parameter setting across a variety of different application domains. REGAL is based on a selection operator, called Universal Suffrage operator, provably allowing the population to asymptotically converge, on the average, to an equilibrium state in which several species coexist. The system is presented in both a serial and a parallel version, and a new distributed computational model is proposed and discussed. The system has been tested on a simple artificial domain for the sake of illustration, and on several complex real-world and artificial domains in order to show its power and to analyze its behavior under various conditions. The results obtained so far suggest that genetic search may be a valuable alternative to logic-based approaches to learning concepts, when no (or little) a priori knowledge is available and a very large hypothesis space has to be explored.