Evolving Drivers for TORCS using On-Line Neuroevolution
Cardamone, L., Loiacono, D., Lanzi P. L. (2009)TR No.: 2009008 | Download PDF | Download PS
We applied on-line neuroevolution to evolve non-player characters for
The Open Racing Car Simulator. While previous approaches allowed on-line
learning with performance improvements during each generation,
our approach enables a finer grained on-line learning with performance
improvements within each lap. We tested our approach on three tracks
using two methods of on-line neuroevolution (NEAT and rtNEAT) combined
with four evaluation strategies […]
Posted: November 2nd, 2009 under Genetic algorithms. Comments: none
Scaling Genetic Algorithms using MapReduce
Verma, A., Llorà, X., Campbell, R.H., Goldberg, D.E. (2009)TR No.: 2009007 | Download PDF | Download PS
Abstract: Genetic algorithms(GAs) are increasingly being applied to large scale problems. The traditional MPI-based parallel GAs do not scale very well. MapReduce is a powerful abstraction developed by Google for making scalable and fault tolerant applications. In this paper, we mould genetic algorithms into the the MapReduce model. We describe the algorithm design and implementation of […]
Posted: October 9th, 2009 under Data-intensive computing, Cloud computing, Genetic algorithms. Comments: none
Coevolution of Pattern Generators and Recognizers
S.W. Wilson (2009)TR No.: 2009006 | Download PDF | Download PS
Proposed is an automatic system for creating pattern
generators and recognizers that may provide new and human-independent
insight into the pattern recognition problem. The system is based on a
three-cornered coevolution of image-transformation programs.
Related PostsMining Oblique Data with XCSMilitary Antenna Design Using Simple and Competent Genetic Algorithms
Posted: June 8th, 2009 under Genetic algorithms. Comments: 1
XCSLib: The XCS Classifier System Library
P.L. Lanzi and D. Loiacono (2009)TR No.: 2009005 | Download PDF | Download PS
The XCS Library (XCSLib) is an open source C++ library for
genetics-based machine learning and learning classifier systems. It
provides (i) several reusable components that can be employed to design
new learning paradigms inspired to the learning classifier system
principles; and (ii) the implementation of two well-known and widely
used models of learning classifier systems.
Related PostsXCSJava 1.0: An implementation of […]
Posted: March 9th, 2009 under Genetic algorithms. Comments: none
The Multi-label OCS with a Genetic Algorithm for Rule Discovery: Implementation and First Results
Vallim, R. M. M., Duque, T. S. P. C., Goldberg, D., Carvalho, A. C. P. L. F. (2009)TR No.: 2009004 | Download PDF | Download PS
Abstract: Learning Classifier Systems (LCSs) are rule-based systems that can be manipulated by a genetic algorithm. LCSs were first designed by Holland to solve classification problems and a lot of effort has been made since then, resulting in a broad number of different algorithms. One of these is called Organizational Classifier System (OCS), a LCSs […]
Posted: February 10th, 2009 under Genetic algorithms. Comments: none
Binary Representation in Gene Expression Programming: Towards a Better Scalability
Moreno-Torres, J. G., Llorà, X., Goldberg, D. E. (2009)TR No.: 2009003 | Download PDF | Download PS
Abstract: One of the main problems that arises when using gene expression programming conditions in learning classifier systems is the increasing number of symbols present as the problem size grows. This issue severely limits the scalability of the technique, due to model building becoming untractable. We propose a binary representation of GEP chromosomes as a […]
Posted: February 10th, 2009 under Genetic algorithms. Comments: 1
ClusterMI: Building Probabilistic Models using Hierarchical Clustering and Mutual Information
Duque, T. S. P. C., Goldberg, D. E. (2009)TR No.: 2009002 | Download PDF | Download PS
Abstract: Genetic Algorithms are a class of metaheuristics with applications on several fields including biology, engineering and even arts. However, simple Genetic Algorithms may suffer from exponential scalability on hard problems.
Estimation of Distribution Algorithms, a special class of Genetic Algorithms, can build complex models of the iterations among variables in the problem, solving several intractable […]
Posted: February 6th, 2009 under Genetic algorithms. Comments: none
Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre
Llorà, X. (2009)TR No.: 2009001 | Download PDF | Download PS
Abstract: Data-intensive computing has positioned itself as a valuable programming paradigm to efficiently approach problems requiring processing very large volumes of data. This paper presents a pilot study about how to apply the data-intensive computing paradigm to evolutionary computation algorithms. Two representative cases—selectorecombinative genetic algorithms and estimation of distribution algorithms—are presented, analyzed, discussed. This study […]
Posted: January 29th, 2009 under Data-intensive computing, Genetic algorithms. Comments: none
Evolutionary Computation in Conceptual Clustering and Tagging
Ueda, T. (2008)TR No.: 2008012 | Download PDF | Download PS
Abstract: The Web 2.0 technologies provide users with collaborative work-spaces over the Internet. For example, Wikipedia is an open source encyclopedia that anyone can edit articles. YouTube provides spaces where users can share videos and annotations about them. Users can put images on Flickers and collaborate each other by categorizing with tagging. These contents are created […]
Posted: May 19th, 2008 under Genetic algorithms. Comments: none
An Analysis of Matching in Learning Classifier Systems
Butz, M.V.,Lanzi, P.L., Llorà, X., Loiacono, D. (2008)TR No.: 2008011 | Download PDF | Download PS
Abstract: We investigate rule matching in learning classifier systems for problems involving binary and real inputs. We consider three rule encodings: the widely used character-based encoding, a specificity-based encoding, and a binary encoding used in Alecsys. We compare the performance of the three algorithms both on matching alone and on typical test problems. The results on […]
Posted: May 15th, 2008 under Genetic algorithms. Comments: none