Evolving Emotional Prosody
Alm, C. O., Llorà, X. (2006)TR No.: 2006018 | Download PDF | Download PS
Abstract:
Emotion is expressed by prosodic cues, and this study uses the active interactive Genetic Algorithm to search a wide space for sad and angry parameters of intensity, F0, and duration in perceptual resynthesis experiments with users. This method avoids large recorded databases and is flexible for
exploring prosodic emotion parameters. Solutions from multiple runs are […]
Posted: March 24th, 2006 under Genetic algorithms. Comments: none
Fast Fitness Implementation of Multiplexer Problems for Pittsburgh LCS
Llorà, X. (2006)TR No.: 2006017 | Download PDF | Download PS
Abstract:
This technical report describes how to compute the fitness of a rule for an arbitrary size multiplexer without doing any instance matching. Pittsburgh-style learning classifier systems require the accuracy and the error of a rule to compute a fitness that promotes maximally accurate and maximally general rules. The accuracy (α) […]
Posted: March 20th, 2006 under Genetic algorithms. Comments: none
Analysis of Ideal Recombination on Random Decomposable Problems
Sastry, K., Pelikan, M., Goldberg, D. E. (2006)TR No.: 2006016 | Download PDF | Download PS
Abstract:
This paper analyzes the behavior of a selectorecombinative genetic algorithm (GA) with an ideal crossover on a class of random additively decomposable problems (rADPs). Specifically, additively decomposable problems of order k whose subsolution fitnesses are sampled from the standard uniform distribution U[0,1] are analyzed. The scalability of the selectorecombinative GA is investigated for 10,000 rADP […]
Posted: March 16th, 2006 under Genetic algorithms. Comments: none
χ-ary Extended Compact Genetic Algorithm for Matlab in C++
Sastry, K., de la Ossa, L., Lobo, F. G. (2006)TR No.: 2006014 | Download PDF | Download PS
Abstract:
This report provides documentation for the χ-ary extended compact genetic algorithm (χECGAm) for matlab in C++ that solves problems with χ-ary alphabets. The fitness function used in the χECGAm is written in matlab. The source code is an extension of the original binary-coded extended compact genetic algorithm (ECGA) (Harik, 1999) and its previous implementation in […]
Posted: March 12th, 2006 under Genetic algorithms. Comments: none
χ-ary Extended Compact Genetic Algorithm in C++
de la Ossa, L., Sastry, K., Lobo, F. G. (2006)TR No.: 2006013 | Download PDF | Download PS
Abstract:
This report provides documentation for the χ-ary extended compact genetic algorithm (χECGA) that solves problems with χ-ary alphabets. The source code is an extension of the original binary-coded extended compact genetic algorithm (ECGA)(Harik, 1999) and its previous implementation in C++
(Lobo & Harik, 1999, Lobo, Sastry, & Harik, 2006). Each decision variable in […]
Posted: March 8th, 2006 under Genetic algorithms. Comments: none
Extended Compact Genetic Algorithm in C++: Version 1.1
Lobo, F.G., Sastry, K., Harik, G. R. (2006)TR No.: 2006012 | Download PDF | Download PS
Abstract:
This report provides documentation for version 1.1 of the extended compact genetic algorithm (ECGA). Version 1.1 uses Mersenne Twister for the pseudo random number generator and is compliant with GCC 3.4 and 4 series.
Related Postsχ-ary Extended Compact Genetic Algorithm in C++χ-ary Extended Compact Genetic Algorithm for Matlab in C++Extended Compact Genetic Algorithm in Matlab
Posted: March 4th, 2006 under Genetic algorithms. Comments: none
The Innovation Pump: Supporting Creative Processes in Collaborative Engineering
Llorà, X. Goldberg, D. E. (2006)TR No.: 2006011 | Download PDF | Download PS
Abstract:
The pervasive expansion of computers and Internet has change the way people collaborate. Terms such as cybercollaboratories are getting traction in day-to-day work. Web boards, blogs, e-mails, and instant messaging have become {\em de facto} mainstream communication channels. People scattered across the globe collaborate thanks to such technologies to carry […]
Posted: February 24th, 2006 under Genetic algorithms. Comments: none
Standard and Averaging Reinforcement Learning in XCS
Lanzi P.L., Loiacono D. (2006)TR No.: 2006010 | Download PDF | Download PS
Abstract:
This paper investigates reinforcement learning (RL) in XCS. First, it
formally shows that XCS implements a method of generalized RL based on
linear approximators, in which the usual input mapping function
translates the state-action space into a niche relative fitness space.
Then, it shows that, although XCS has always been related to standard
RL, XCS is actually a method of […]
Posted: February 20th, 2006 under Genetic algorithms. Comments: none
Classifier Prediction based on Tile Coding
Lanzi P.L., Loiacono D., Wilson S.W., Goldberg D.E. (2006)TR No.: 2006009 | Download PDF | Download PS
Abstract:
This paper introduces XCSF extended with tile coding prediction: each
classifier implements a tile coding approximator; the genetic algorithm
is used to adapt both classifier conditions (i.e., to partition the
problem) and the parameters of each approximator; thus XCSF evolves an
ensemble of tile coding approximators instead of the typical monolithic
approximator used in reinforcement learning. The paper reports a
comparison […]
Posted: February 16th, 2006 under Genetic algorithms. Comments: none
Prediction Update Algorithms for XCSF: RLS, Kalman Filter, and Gain Adaptation
Lanzi P.L., Loiacono D., Wilson S.W., Goldberg D.E. (2006)TR No.: 2006008 | Download PDF | Download PS
Abstract:
The performance of XCSF relies on (i) the evolutionary pressure toward
accurate maximally general classifiers; (ii) the type of approximator
used to compute classifier prediction; and (iii) the estimation
algorithm used to update the classifier prediction parameters. In this
paper we study how different prediction update algorithms influence the
performance of XCSF. We consider three classical parameter estimation
algorithms (NLMS, RLS, […]
Posted: February 12th, 2006 under Genetic algorithms. Comments: none