Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Using genetic algorithms to learn disjunctive rules from examples
Proceedings of the seventh international conference (1990) on Machine learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The Utility of Knowledge in Inductive Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
A Knowledge-Intensive Genetic Algorithm for Supervised Learning
Machine Learning - Special issue on genetic algorithms
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
Declarative Bias for Specific-to-General ILP Systems
Machine Learning - Special issue on bias evaluation and selection
Scaling up inductive learning with massive parallelism
Machine Learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Exploring the Power of Genetic Search in Learning Symbolic Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Multiple Learning Strategies in First Order Logics
Machine Learning - Special issue on multistrategy learning
The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
Does Data-Model Co-evolution Improve Generalization Performance of Evolving Learners?
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Learning Phonetic Rules in a Speech Recognition System
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A Theoretical Investigation of a Parallel Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Search-intensive concept induction
Evolutionary Computation
An analysis of the “universal suffrage” selection operator
Evolutionary Computation
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
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Concept learning is a computationally demanding task that involves searching large hypothesis spaces containing candidate descriptions. Stochastic search combined with parallel processing provide a promising approach to successfully deal with such computationally intensive tasks.Learning systems based on distributed genetic algorithms (GA) were able to find concept descriptions as accurate as the ones found by state-of-the-art learning systems based on alternative approaches. However, genetic algorithms' exploitation has the drawback of being computationally demanding.We show how a suitable architectural choice, named cooperative evolution, allows to solve complex applications in an acceptable user waiting time and with a reasonable computational cost by using GA-based learning systems because of the effective exploitation of distributed computation. A variety of experimental settings is analyzed and an explanation for the empirical observations is proposed.