Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
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
Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Seven methods for transforming corporate data into business intelligence
Seven methods for transforming corporate data into business intelligence
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Mining Very Large Databases with Parallel Processing
Mining Very Large Databases with Parallel Processing
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Genetic Algorithm for Optimizing Fuzzy Decision Trees
Proceedings of the 6th International Conference on Genetic Algorithms
A Parallel Genetic Algorithm for Concept Learning
Proceedings of the 6th International Conference on Genetic Algorithms
Discovery of Surprising Exception Rules Based on Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
A Relational Data Mining Tool Based On Genetic Programming
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Evolving a Generalized Behaviour: Artificial Ant Problem Revisited
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Search-intensive concept induction
Evolutionary Computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
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
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
Artificial Intelligence Review
A Genetic Algorithm for Text Classification Rule Induction
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
A novel two level evolutionary approach for classification rules extraction
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Artificial Intelligence in Medicine
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This chapter addresses the integration of knowledge discovery in databases (KDD) and evolutionary algorithms (EAs), particularly genetic algorithms and genetic programming. First, we provide a brief overview of EAs. Then the remaining text is divided into three parts. Section 2 discusses the use of EAs for KDD. The emphasis is on the use of EAs for discovering high-level prediction rules, but we also discuss the use of EAs in attribute selection and in the optimization of parameters for other kinds of KDD algorithms (such as decision trees and nearest neighbor algorithms). Section 3 discusses three research problems in the design of an EA for KDD, namely, how to discover comprehensible rules with genetic programming, how to discover surprising (interesting) rules, and how to scale up EAs with parallel processing. Finally, Section 4 discusses what the added value of KDD is for EAs. This section includes the remark that generalization performance on a separate test set (unseen during training, or EA run) is a basic principle for evaluating the quality of discovered knowledge, and then suggests that this principle should be followed in other EA applications.