Catálogo de publicaciones - libros

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.

This chapter considers just two countries in the Asia-Pacific region, India and Australia. Policies are outlined that show how the governments in these countries are attempting to encourage the IT industry, eBusiness and eGovernment, address problems of Internet access, and regulate Internet content.

The contribution describes a new regional initiative born in collaboration between certain national computer societies and the International Federation for Information Processing. A new, rather informal, regional body has been set up — the Information Technology Standing Regional Committee for Regional Collaboration. Its purpose is to facilitate the cooperative interaction of IT professionals regardless of their momentary employment or any other kind of affiliation. The prerequisites are a professional capacity and a wish to take part in certain kinds of projects. This initiative has attracted rather wide attention and, hopefully, may also be adopted as a paradigm to be used elsewhere wherever such regional collaboration is appreciated.

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes the problem NP-hard and, for our experiments, greatly improves on sub-optimal solutions of established methods. We compare two different genotype encodings, which tradeoff search space size with fitness landscape, as well as the associated genetic operators. Our finding favors a smaller encoding despite its fewer intermediate solutions and demonstrates the impact of the modularity enforced by genetic operators on the performance of the algorithm.

This paper sets out to analyze the information competition element. Prior to a review of the roots and of the two parts of the current intensifying information society race, we will take a look at the historic prelude, the functioning of some pre-information societies — that is to say we will glean from the patterns of the making of competitive advantage in information among nations from the early high cultures to the middle of the 20 century. As a “prehistory”, we will outline the emergence of the information society and its development into a competition problem: principally the movement of the American-Japanese tandem (1961–1978) and the pursuing bunch (1978–1991). And finally, we will analyze the decade (1992–2002) of comprehensive national information strategies, demonstrating that in the measurable domain of the information society there really are winners and losers: systematic information society development programs have tangible outcomes. The gap is widening, and the developed countries are winners every time. We can observe the real information society race taking place between them.

This critical appraisal of the role and impact of the information society model in the United States educational system begins with a discussion of three dominant paradigms: automation, “information society,” and “mind tools;” and then traces the history of information and communication technology (ICT) in education in the United States, describing how ideas, intertwined with the conceptions of the information society, led to distractions that have impeded vigorous development of ICT to improve education. These distractions included the claim that schools should take up the responsibility of training all students in ICT for the so-called information workforce. Promoters of the information society model together with the marketing arms of the IT industry perpetuated the adoption of ICT as an end in itself rather than as a means toward improving the main business of education, learning. These pressures over the past three decades have left the ICT in education in a state of confusion, lacking adequate support and, in many instances, the infrastructure to adapt to the demands of the twenty-first century.

This chapter looks at the position occupied by the home as the central focus of the information society. A working definition of the home is used and various interactions and activities that take place in the home are discussed with relation to the home, and its changing place in the move to an information society. The chapter also discusses various information activities that impinge upon the function of everyday life in the home including the use of information and information systems. Finally some of the consequences of the move to an information-based model of the home are outlined.