This paper gives an overview of the current state of ASoC design methodology and presents preliminary results on evaluating the learning classifier system XCS for the control of a QuadCore. The ASoC design methodology can determine system reliability based on activity, power and temperature analysis, together with reliability block diagrams. The evaluation of the XCS shows that in the evaluated...

It has been shown empirically that the XCS classifier system solves typical classification problems in a machine learning competitive way. However, until now, no learning time estimate has been derived analytically for the system. This paper introduces a time estimate that bounds the learning time of XCS until maximally accurate classifiers are found. We assume a domino convergence model in whi...

XCS is a classi er system recently introduced by Wilson that differs from Holland's framework in that classi er tness is based on the accuracy of the prediction instead of the prediction itself. According to the original proposal, XCS has no internal message list as traditional classi er systems does; hence XCS learns only reactive input/output mappings that are optimal in Markovian environment...

EDITORIAL Editorial I t is a great pleasure and a great honor for me to introduce this new issue of SIGEVOlution that hosts an interview with Stewart W. Wilson. To me, Stewart is a mentor and a great friend. To our community, he is the person who, with XCS and XCSF, singlehandedly revolutionized learning classifier system research. In 1994, his paper on XCS in the Evolutionary Computation Journ...

The extended classifier systems (XCS) by producing a set of rules is (classifier) trying to solve learning problems as online. XCS is a rather complex combination of genetic algorithm and reinforcement learning that using genetic algorithm tries to discover the encouraging rules and value them by reinforcement learning. Among the important factors in the performance of XCS is the possibility to...

In this paper, we evaluate the feasibility of using the learning classifier XCS to control a System-on-Chip. Increasing number of transistors and process variation make it difficult for a chip designer to foresee all possible run-time conditions. Postponing some decisions from design time to run time alleviates the designer’s life and allows shorter time-to-market. In this paper, we evaluate if...

In many classifier systems, the classifier strength parameter serves as a predictor of future payoff and as the classifier’s fitness for the genetic algorithm. We investigate a classifier system, XCS, in which each classifier maintains a prediction of expected payoff, but the classifier’s fitness is given by a measure of the prediction’s accuracy. The system executes the genetic algorithm in ni...

We present a list of 15 clusters of galaxies, serendipitously detected by the XMM Cluster Survey (XCS), that have a high probability of detection by the Planck satellite. Three of them already appear in the Planck Early Sunyaev–Zel’dovich (ESZ) catalogue. The estimation of the Planck detection probability assumes the flat Lambda cold dark matter ( CDM) cosmology most compatible with 7-year Wilk...

This paper extends the work presented in (Kovacs, 1996) on evolving optimal solutions to boolean reinforcement learning problems using Wilson's recent XCS classiier system. XCS forms complete mappings of the payoo environment in the reinforcement learning tradition thanks to its accuracy based tness, which, according to Wilson's Generalization Hypothesis, also gives XCS a tendency towards accur...

| We add internal memory to the XCS classiier system. We then test XCS with internal memory, named XCSM, in non-Markovian environments with two and four aliasing states. Experimental results show that XCSM can easily converge to optimal solutions in simple environments; moreover, XCSM's performance is very stable with respect to the size of the internal memory involved in learning. However, the...