Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
ACM SIGEVOlution
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Automated alphabet reduction method with evolutionary algorithms for protein structure prediction
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Do not match, inherit: fitness surrogates for genetics-based machine learning techniques
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Learning Classifier Systems in Data Mining
Learning Classifier Systems in Data Mining
An analysis of matching in learning classifier systems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
Knowledge-based fast evaluation for evolutionary learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Domain of competence of XCS classifier system in complexity measurement space
IEEE Transactions on Evolutionary Computation
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
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We are living in the peta-byte era.We have larger and larger data to analyze, process and transform into useful answers for the domain experts. Robust data mining tools, able to cope with petascale volumes and/or high dimensionality producing human-understandable solutions are key on several domain areas. Genetics-based machine learning (GBML) techniques are perfect candidates for this task, among others, due to the recent advances in representations, learning paradigms, and theoretical modeling. If evolutionary learning techniques aspire to be a relevant player in this context, they need to have the capacity of processing these vast amounts of data and they need to process this data within reasonable time. Moreover, massive computation cycles are getting cheaper and cheaper every day, allowing researchers to have access to unprecedented parallelization degrees. Several topics are interlaced in these two requirements: (1) having the proper learning paradigms and knowledge representations, (2) understanding them and knowing when are they suitable for the problem at hand, (3) using efficiency enhancement techniques, and (4) transforming and visualizing the produced solutions to give back as much insight as possible to the domain experts are few of them. This tutorial will try to answer this question, following a roadmap that starts with the questions of what large means, and why large is a challenge for data mining methods. Afterwards, we will discuss different facets in which we can overcome this challenge: Efficiency enhancement techniques, representations able to cope with large dimensionality spaces, scalability of learning paradigms, hardware solutions, parallel models and data-intensive computing. The roadmap continues with examples of real applications of GBML systems and finishes with an analysis of further directions.