Autonomic Networking Integrated Model and Approach (ANIMA)

This chapter presents the work of the Autonomic Networking Integrated Model and Approach (ANIMA) working group of the Internet Engineering Task Force (IETF). It was formed to standardize protocols and procedures for an ANIMA autonomic network (AN) and first chartered to define the ANIMA secure autonomic network infrastructure (ANI). This chapter describes the technical history and goals leading to this working group. It then describes how the ANIMA approach provides an evolutionary approach to securing and automating networks and to provide a common infrastructure to evolve into future autonomic networks. Finally, this chapter compares this approach to adjacent standards technologies and discusses interesting next steps.

V. N. Tatishchev: Prolegomena of the Russian modernization research program in the XVIII century (Institutional-evolutionary approach)

Introduction. V. N. Tatishchev, one of the founders of the Russian history studies, was notable for his broad views on the evolution of society and economic order. His economic views were not widely discussed during his lifetime and were not much in demand afterwards. Familiarity with his major works is hampered by the fact that they were almost never published in the form of notes, letters, and manuscripts. The ambiguity of his approaches, conclusions, recommendations and, accordingly, their evaluation was noted by many researchers who took diametrically opposed views. Deep erudition, reliance on Western European philosophy and Russian theology allowed the enlightener to create the conceptual milestones of the future institutional program. Theoretical analysis. Modernization of society should be based on constant changes in existing legislative and economic practices, ideological perceptions, and cultural patterns. This approach allows us to identify the most effective institutions (formal and informal rules), taking into account national specifics. Methodologically, the relationship between changes in public administration and social ethos “vertically and horizontally” is established; the importance of societal economic culture as a factor of sustainable development is emphasized. Empirical analysis. Considered chronologically consecutive works on purely economic topics and legal foundations of power are supported by a significant array of letters to Peter I, the Academy of Sciences, the Berg Collegium, and public figures of the first half of the 18th century. According to the thinker, economic policy, both at micro and macro levels, should be based on regulations, organizational adaptation and rational borrowing. The qualitative description of the structure of social relations of absolutist Russia, in the form of “physiology of society”, which resonates with the modern concepts in economic sociology and new institutional economic theory, is highlighted. Results. V. N. Tatishchev can reasonably be considered the conceptual forerunner of the modern theory of institutionalism. As an enlightener, in the spirit of eighteenth-century social thought, he created an introduction to the importance of permanent changes in Russian economic and social structures. The imperative of state construction of the economy at the macro level is supported by attention to micro-changes in the form of regular economic practices, combining elements of originality and creative borrowing of foreign innovations. Evolutionary approach of the thinker echoes the formation and development of economic views of the XIX and XX centuries, especially in the prerequisites of the theory of history periodization and the transition from one political order to another on the basis of changes in institutions (formal and informal rules).

DISCUSSION: THE EXTENSION OF SYSTEM BOUNDARIES IN SHIP DESIGN

This paper is to be welcomed as it argues for the broadening of ship design from what has generally been too narrow a focus in most cases due to the ship design process being a largely evolutionary approach. Given current environmental preoccupations, the authors’ approach is strongly driven by concerns over sustainability, in contrast some other broadening approaches have, for example, done so with an emphasis on ship architecture [23, 24]. Thus the authors’ approach seeks to make the profession recognise that even at the preliminary stages of ship design that it is complex and challenging but more appropriate in having a wider boundary than a directly (or simplistically) economic one.

Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment

<p>Scheduling is an important problem in artificial intelligence and operations research. In production processes, it deals with the problem of allocation of resources to different tasks with the goal of optimizing one or more objectives. Job shop scheduling is a classic and very common scheduling problem. In the real world, shop environments dynamically change due to events such as the arrival of new jobs and machine breakdown. In such manufacturing environments, uncertainty in shop parameters is typical. It is of vital importance to develop methods for effective scheduling in such practical settings. Scheduling using heuristics like dispatching rules is very popular and suitable for such environments due to their low computational cost and ease of implementation. For a dynamic manufacturing environment with varying shop scenarios, using a universal dispatching rule is not very effective. But manual development of effective dispatching rules is difficult, time consuming and requires expertise. Genetic programming is an evolutionary approach which is suitable for automatically designing effective dispatching rules. Since the genetic programming approach searches in the space of heuristics (dispatching rules) instead of building up a schedule, it is considered a hyper-heuristic approach. Genetic programming like many other evolutionary approaches is computationally expensive. Therefore, it is of vital importance to present the genetic programming based hyper-heuristic (GPHH) system with scheduling problem instances which capture the complex shop scenarios capturing the difficulty in scheduling. Active learning is a related concept from machine learning which concerns with effective sampling of those training instances to promote the accuracy of the learned model. The overall goal of this thesis is to develop effective and efficient genetic programming based hyper-heuristic approaches using active learning techniques for dynamic job shop scheduling problems with one or more objectives. This thesis develops new representations for genetic programming enabling it to incorporate the uncertainty information about processing times of the jobs. Furthermore, a cooperative co-evolutionary approach is developed for GPHH which evolves a pair of dispatching rules for bottleneck and non-bottleneck machines in the dynamic environment with uncertainty in processing times arising due to varying machine characteristics. The results show that the new representations and training approaches are able to significantly improve the performance of evolved dispatching rules. This thesis develops a new GPHH framework in order to incorporate active learning methods toward sampling DJSS instances which promote the evolution of more effective rules. Using this framework, two new active sampling methods were developed to identify those scheduling problem instances which promoted evolution of effective dispatching rules. The results show the advantages of using active learning methods for scheduling under the purview of GPHH. This thesis investigates a coarse-grained model of parallel evolutionary approach for multi-objective dynamic job shop scheduling problems using GPHH. The outcome of the investigation was utilized to extend the coarse-grained model and incorporate an active sampling heuristic toward identifying those scheduling problem instances which capture the conflict between the objectives. The results show significant improvement in the quality of the evolved Pareto set of dispatching rules. Through this thesis, the following contributions have been made. (1) New representations and training approaches for GPHH to incorporate uncertainty information about processing times of jobs into dispatching rules to make them more effective in a practical shop environment. (2) A new GPHH framework which enables active sampling of scheduling problem instances toward evolving dispatching rules effective across complex shop scenarios. (3) A new active sampling heuristic based on a coarse-grained model of parallel evolutionary approach for GPHH for multi-objective scheduling problems.</p>

Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment

<p>Scheduling is an important problem in artificial intelligence and operations research. In production processes, it deals with the problem of allocation of resources to different tasks with the goal of optimizing one or more objectives. Job shop scheduling is a classic and very common scheduling problem. In the real world, shop environments dynamically change due to events such as the arrival of new jobs and machine breakdown. In such manufacturing environments, uncertainty in shop parameters is typical. It is of vital importance to develop methods for effective scheduling in such practical settings. Scheduling using heuristics like dispatching rules is very popular and suitable for such environments due to their low computational cost and ease of implementation. For a dynamic manufacturing environment with varying shop scenarios, using a universal dispatching rule is not very effective. But manual development of effective dispatching rules is difficult, time consuming and requires expertise. Genetic programming is an evolutionary approach which is suitable for automatically designing effective dispatching rules. Since the genetic programming approach searches in the space of heuristics (dispatching rules) instead of building up a schedule, it is considered a hyper-heuristic approach. Genetic programming like many other evolutionary approaches is computationally expensive. Therefore, it is of vital importance to present the genetic programming based hyper-heuristic (GPHH) system with scheduling problem instances which capture the complex shop scenarios capturing the difficulty in scheduling. Active learning is a related concept from machine learning which concerns with effective sampling of those training instances to promote the accuracy of the learned model. The overall goal of this thesis is to develop effective and efficient genetic programming based hyper-heuristic approaches using active learning techniques for dynamic job shop scheduling problems with one or more objectives. This thesis develops new representations for genetic programming enabling it to incorporate the uncertainty information about processing times of the jobs. Furthermore, a cooperative co-evolutionary approach is developed for GPHH which evolves a pair of dispatching rules for bottleneck and non-bottleneck machines in the dynamic environment with uncertainty in processing times arising due to varying machine characteristics. The results show that the new representations and training approaches are able to significantly improve the performance of evolved dispatching rules. This thesis develops a new GPHH framework in order to incorporate active learning methods toward sampling DJSS instances which promote the evolution of more effective rules. Using this framework, two new active sampling methods were developed to identify those scheduling problem instances which promoted evolution of effective dispatching rules. The results show the advantages of using active learning methods for scheduling under the purview of GPHH. This thesis investigates a coarse-grained model of parallel evolutionary approach for multi-objective dynamic job shop scheduling problems using GPHH. The outcome of the investigation was utilized to extend the coarse-grained model and incorporate an active sampling heuristic toward identifying those scheduling problem instances which capture the conflict between the objectives. The results show significant improvement in the quality of the evolved Pareto set of dispatching rules. Through this thesis, the following contributions have been made. (1) New representations and training approaches for GPHH to incorporate uncertainty information about processing times of jobs into dispatching rules to make them more effective in a practical shop environment. (2) A new GPHH framework which enables active sampling of scheduling problem instances toward evolving dispatching rules effective across complex shop scenarios. (3) A new active sampling heuristic based on a coarse-grained model of parallel evolutionary approach for GPHH for multi-objective scheduling problems.</p>

Sztuka zwierząt i „paradygmat siatkówkowy” — czym nie jest estetyka ewolucyjna

In the article I defend some of the thesis presented in my book ‘Art as Adaptation: Universalism in Evolutionary Aesthetics’ (Sztuka jako adaptacja: uniwersalizm w estetyce ewolucyjnej) (2018) against the claims of my critics. I focus especialy on some misreadings regarding the explanatory power of evolutionary science. I try to show that even though evolutionarily informed aesthetics is not a handy tool for analyzing the intrinsically diverse currents of modern and neo-avant-garde art, it does an excellent job of explaining the mental tendencies and typical behaviors behind these practices. I also focus on the artistic abilities of animals and the problematic dominance of the visuality paradigm in the evolutionary approach, topics that are unjustifiably considered to be most momentous in evolutionary aesthetics.