The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
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
Analysis of the initialization stage of a Pittsburgh approach learning classifier system
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Design and Analysis of Learning Classifier Systems: A Probabilistic Approach (Studies in Computational Intelligence)
Modeling UCS as a mixture of experts
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Learning concept classification rules using genetic algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Binary rule encoding schemes: a study using the compact classifier system
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
How XCS deals with rarities in domains with continuous attributes
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Resource management and scalability of the XCSF learning classifier system
Theoretical Computer Science
Large scale data mining using genetics-based machine learning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Large scale data mining using genetics-based machine learning
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Models in Genetic Based Machine Learning (GBML) systems are commonly used to gain understanding of how the system works and, as a consequence, adjust it better. In this paper we propose models for the probability of having a good initial population using the Attribute List Knowledge Representation (ALKR) for discrete inputs using the GABIL encoding. We base our work in the schema and covering bound models previously proposed for XCS. The models are extended to (a) deal with the combination of ALKR+GABIL representation, (b) explicitly handle datasets with niche overlap and (c) model the impact of using covering and a default rule in the representation. The models are designed and evaluated within the framework of the BioHEL GBML system and are empirically evaluated using first boolean datasets and later also nominal datasets of higher cardinality. The models in this paper allow us to evaluate the challenges presented by problems with high cardinality (in terms of number of attributes and values of the attributes) as well as the benefits contributed by each of the components of BioHEL's representation and initialisation operators.