Machine Learning Approaches to Estimating Software Development Effort
IEEE Transactions on Software Engineering
Using Disruptive Selection to Maintain Diversity in GeneticAlgorithms
Applied Intelligence
Introduction to the Special Section on Learning in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Learning Classifier Systems Bibliography
Learning Classifier Systems, From Foundations to Applications
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Application of Multi Objective Evolutionary Algorithms to Analogue Filter Tuning
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Characterization of aggregate fuzzy membership functions using Saaty's eigenvalue approach
Computers and Operations Research
Recent trends in learning classifier systems research
Advances in evolutionary computing
Ordered incremental training for GA-based classifiers
Pattern Recognition Letters
Journal of Systems and Software
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Scheduling of genetic algorithms in a noisy environment
Evolutionary Computation
Computers and Electronics in Agriculture
Functional genetic programming and exhaustive program search with combinator expressions
International Journal of Knowledge-based and Intelligent Engineering Systems - Genetic Programming An Emerging Engineering Tool
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Supervised Segmentation of the Cervical Cell Images by using the Genetic Algorithms
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Evolving strategies using the nearest-neighbor rule and a genetic algorithm
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
An Adaptive Agent Model Estimating Human Trust in Information Sources
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Expert Systems with Applications: An International Journal
Design of a route guidance system with shortest driving time based on genetic algorithm
ACACOS'11 Proceedings of the 10th WSEAS international conference on Applied computer and applied computational science
Multi-objective GA rule extraction in a parallel framework
Proceedings of the 15th WSEAS international conference on Computers
On combining fractal dimension with GA for feature subset selecting
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Supervised genetic search for parameter selection in painterly rendering
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Genetic paint: a search for salient paintings
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Mutagenic Primer Design for Mismatch PCR-RFLP SNP Genotyping Using a Genetic Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Constructing petri net models using genetic search
Mathematical and Computer Modelling: An International Journal
Recursive Learning of Genetic Algorithms with Task Decomposition and Varied Rule Set
International Journal of Applied Evolutionary Computation
Novel hybrid genetic algorithm for progressive multiple sequence alignment
International Journal of Bioinformatics Research and Applications
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Genetic algorithms represent a class of adaptive search techniques that have been intensively studied in recent years. Much of the interest in genetic algorithms is due to the fact that they provide a set of efficient domain-independent search heuristics which are a significant improvement over traditional “weak methods” without the need for incorporating highly domain-specific knowledge. There is now considerable evidence that genetic algorithms are useful for global function optimization and NP-hard problems. Recently, there has been a good deal of interest in using genetic algorithms for machine learning problems. This paper provides a brief overview of how one might use genetic algorithms as a key element in learning systems.