• Publications
  • Influence
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
TLDR
This paper proposes a method to combat user fatigue by augmenting user evaluations with a synthetic fitness function. Expand
  • 110
  • 12
  • PDF
Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
TLDR
This paper introduces a new semantic-driven data-intensive flow infrastructure which: (1) provides a robust and transparent scalable solution from a laptop to large-scale clusters,(2) creates an unified solution for batch and interactive tasks in high-performance computing environments, and (3) encourages reusing and sharing components. Expand
  • 67
  • 8
Scaling Genetic Algorithms Using MapReduce
TLDR
In this paper, we show how genetic algorithms can be modeled into the MapReduce model. Expand
  • 162
  • 6
  • PDF
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
TLDR
This paper compares the learning performance, in terms of prediction accuracy, of two genetic-based learning systems, XCS and GALE, with six well-known learning algorithms, coming from instance based learning, decision tree induction, rule-learning, statistical modeling and support vector machines. Expand
  • 120
  • 6
Knowledge-independent data mining with fine-grained parallel evolutionary algorithms
TLDR
This paper illustrates the application of evolutionary algorithms (EA) to data mining problems. Expand
  • 79
  • 4
  • PDF
Fast rule matching for learning classifier systems via vector instructions
TLDR
This paper presents efficient condition encoding and fast rule matching strategies using vector instructions for Michigan-style learning classifier systems. Expand
  • 35
  • 4
  • PDF
Towards billion-bit optimization via a parallel estimation of distribution algorithm
TLDR
This paper presents a highly efficient, fully parallelized implementation of the compact genetic algorithm (cGA) to solve very large scale problems with millions to billions of variables. Expand
  • 54
  • 3
  • PDF
Speeding-Up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy
TLDR
We develop a theoretical framework for a windowing scheme called ILAS, developed previously by the authors. Expand
  • 44
  • 3
  • PDF
The compact classifier system: scalability analysis and first results
TLDR
This paper presents an analysis of how maximally general and accurate rules can be evolved in a Pittsburgh-style classifier system. Expand
  • 27
  • 3
Bounding the Effect of Noise in Multiobjective Learning Classifier Systems
TLDR
This paper analyzes the impact of using noisy data sets on a Pittsburgh-style learning classifier system based on multiobjective selection. Expand
  • 31
  • 3