Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Never Too Old To Learn: On-line Evolution of Controllers in Swarm- and Modular Robotics

Eiben, A.E. [Promotor

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Why (Almost) Everything that Happened was Constitutional: Towards a New Paradigm for Constitutional Theory and History with Case Studies on the English Royal Minorities

This thesis is a defence of constitutional history – a once prosperous field of study in the UK, but now fallen into disrepute and neglect. The first question addressed by this thesis, therefore, is: Why has constitutional history fallen from favour? It is argued that it is principally due to a fundamental and widespread misunderstanding as to the nature of constitutions and laws, and how these things change over time. Moreover, it is due to the field’s association with a group of nineteenth-century writers, elements of whose approach and assumptions have – rightly – come to be regarded as outmoded and distasteful. These things need to be put aright if constitutional history is to be revived. Certainly, there has been some movement to resurrect the field, but this is unlikely to be successful or profitable without a solid underlying constitutional theory. This is still lacking. This thesis, therefore, seeks to provide that solid underlying constitutional theory. It does so by suggesting an interpretative framework for constitutional theory and history in Part I. This consists of three theories: the Theory of Constitutional Ubiquity; the Associational Theory of Law; and the Generational Theory of Law. These are complemented by a chapter on the theory of succession and two appendices. Together, these argue that all societies – regardless as to time or place – have constitutions, which can and do change over time. Consequently, there is no reason why constitutional history should be disregarded. That it has been is regrettable. Parts II and III seek, firstly, to show the framework’s validity and explanatory power, and, secondly, to show how constitutional history might look in the future. They do this by considering constitutions in, primarily, mediaeval thought and practice – the mediaeval period being a time in which the existence of constitutions is often dismissed or, at least, viewed with considerable scepticism. Part II, concerning mediaeval thought, argues that people throughout the mediaeval period were primed to think about constitutional matters. Part III considers mediaeval and early modern practice through the lens of succession and governance vis-à-vis the royal minorities of mediaeval England. It argues that contemporary practice shows clear regard for what can rightfully be called a constitution, though it changed from generation to generation. Having charted constitutional history’s rise and fall, and having made the argument for its resuscitation based on the principles set out in the framework, the thesis concludes by considering constitutional history’s future. There is, or seems to be, cause for optimism

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp