except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) A life spent making mistakes is not only more honorable, but more useful than a life spent doing nothing. PrefaceAny sufficiently advanced technology is indistinguishable from magic.Sir Arthur Charles Clarke μGP is a computational approach for autonomously pursuing a goal defined by the user. To this end, candidate solutions for the given task are repeatedly modified, evaluated and enhanced. The alteration process mimics some principles of the Neo-Darwinian paradigm, such as variation, inheritance, and selection. μGP has been developed in Politecnico di Torino since 2000. Its original application was the generation of assembly-language programs for different types of microprocessors, hence the Greek letter micro in the name. Its name is sometimes spelled MicroGP or uGP due to typographic limitations. μGP is free software: it can be redistributed and modified under the terms of the GNU General Public License 1 . μGP is ordinarily utilized to find the optimal solution of hard problems, and it has been demonstrated able to outperform both human experts and conventional heuristics in such a task. In order to exploit the approach, the user describes the appearance of the solutions to his problem and provides a program able to evaluate them. The tool implementing the approach fosters a set of random solutions, and iteratively refines them in discrete steps. Its heuristic local-search algorithm uses the result of the evaluations, together with other internal information, to focus on the regions of the search space that look more promising, and eventually to produce an optimal solution. μGP is an evolutionary algorithm. Different candidate solutions are considered in each step of the search process, and new ones are generated through mechanisms that ape both sexual and asexual reproduction. New solutions inherit distinctive traits from existing ones, and may coalesce the good characteristics of different parents. Better solutions have a greater chance to reproduce, and to succeed in the simulated struggle for existence.Candidate solutions are internally encoded as graphs, or, more precisely, as directed multigraphs 2 . During the search process, multigraphs are constrained by a user-defined set of rules to conform to sensible structures. They are transformed 1 For more information, and how to apply and follow the GNU GPL, see http://www.gnu.org/ licenses/ 2 A directed multigraph is a graph where a direction is assigned...
Test of peripheral modules has not yet been deeply investigated by the research community. When embedded in a system on a chip, peripheral cores introduce new issues for post-production testing. A peripheral core embedded in a SoC requires a test set able to properly perform two different tasks: configure the device in different operation modes and properly exercise it. In this paper an automatic approach able to generate test sets for peripheral cores embedded in a SoC is described. The presented approach is based on an evolutionary algorithm that exploits high-level simulation and gathers coverage metrics information to produce the test sets. The method compares favorably with results obtained by hand.
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