ISPOL'ZOVANIYE SOVREMENNYKH METODOV ELEKTRONNOGO OBUCHENIYA V VUZE

The article discusses the forms and methods that are optimal for use in e-learning: heuristic, research, game, diagnostic, information-receptive and reproductive. The e-learning environment today represents a wide range of educational technologies, methods and tools that can be used to organize full-fledged education at a university.

Adapting a Hyper-heuristic to Respond to Scalability Issues in Combinatorial Optimisation

<p>The development of a heuristic to solve an optimisation problem in a new domain, or a specific variation of an existing problem domain, is often beyond the means of many smaller businesses. This is largely due to the task normally needing to be assigned to a human expert, and such experts tend to be scarce and expensive. One of the aims of hyper-heuristic research is to automate all or part of the heuristic development process and thereby bring the generation of new heuristics within the means of more organisations. A second aim of hyper-heuristic research is to ensure that the process by which a domain specific heuristic is developed is itself independent of the problem domain. This enables a hyper-heuristic to exist and operate above the combinatorial optimisation problem “domain barrier” and generalise across different problem domains. A common issue with heuristic development is that a heuristic is often designed or evolved using small size problem instances and then assumed to perform well on larger problem instances. The goal of this thesis is to extend current hyper-heuristic research towards answering the question: How can a hyper-heuristic efficiently and effectively adapt the selection, generation and manipulation of domain specific heuristics as you move from small size and/or narrow domain problems to larger size and/or wider domain problems? In other words, how can different hyperheuristics respond to scalability issues? Each hyper-heuristic has its own strengths and weaknesses. In the context of hyper-heuristic research, this thesis contributes towards understanding scalability issues by firstly developing a compact and effective heuristic that can be applied to other problem instances of differing sizes in a compatible problem domain. We construct a hyper-heuristic for the Capacitated Vehicle Routing Problem domain to establish whether a heuristic for a specific problem domain can be developed which is compact and easy to interpret. The results show that generation of a simple but effective heuristic is possible. Secondly we develop two different types of hyper-heuristic and compare their performance across different combinatorial optimisation problem domains. We construct and compare simplified versions of two existing hyper-heuristics (adaptive and grammar-based), and analyse how each handles the trade-off between computation speed and quality of the solution. The performance of the two hyper-heuristics are tested on seven different problem domains compatible with the HyFlex (Hyper-heuristic Flexible) framework. The results indicate that the adaptive hyper-heuristic is able to deliver solutions of a pre-defined quality in a shorter computational time than the grammar-based hyper-heuristic. Thirdly we investigate how the adaptive hyper-heuristic developed in the second stage of this thesis can respond to problem instances of the same size, but containing different features and complexity. We investigate how, with minimal knowledge about the problem domain and features of the instance being worked on, a hyper-heuristic can modify its processes to respond to problem instances containing different features and problem domains of different complexity. In this stage we allow the adaptive hyper-heuristic to select alternative vectors for the selection of problem domain operators, and acceptance criteria used to determine whether solutions should be retained or discarded. We identify a consistent difference between the best performing pairings of selection vector and acceptance criteria, and those pairings which perform poorly. This thesis shows that hyper-heuristics can respond to scalability issues, although not all do so with equal ease. The flexibility of an adaptive hyper-heuristic enables it to perform faster than the more rigid grammar-based hyper-heuristic, but at the expense of losing a reusable heuristic.</p>

Adapting a Hyper-heuristic to Respond to Scalability Issues in Combinatorial Optimisation

<p>The development of a heuristic to solve an optimisation problem in a new domain, or a specific variation of an existing problem domain, is often beyond the means of many smaller businesses. This is largely due to the task normally needing to be assigned to a human expert, and such experts tend to be scarce and expensive. One of the aims of hyper-heuristic research is to automate all or part of the heuristic development process and thereby bring the generation of new heuristics within the means of more organisations. A second aim of hyper-heuristic research is to ensure that the process by which a domain specific heuristic is developed is itself independent of the problem domain. This enables a hyper-heuristic to exist and operate above the combinatorial optimisation problem “domain barrier” and generalise across different problem domains. A common issue with heuristic development is that a heuristic is often designed or evolved using small size problem instances and then assumed to perform well on larger problem instances. The goal of this thesis is to extend current hyper-heuristic research towards answering the question: How can a hyper-heuristic efficiently and effectively adapt the selection, generation and manipulation of domain specific heuristics as you move from small size and/or narrow domain problems to larger size and/or wider domain problems? In other words, how can different hyperheuristics respond to scalability issues? Each hyper-heuristic has its own strengths and weaknesses. In the context of hyper-heuristic research, this thesis contributes towards understanding scalability issues by firstly developing a compact and effective heuristic that can be applied to other problem instances of differing sizes in a compatible problem domain. We construct a hyper-heuristic for the Capacitated Vehicle Routing Problem domain to establish whether a heuristic for a specific problem domain can be developed which is compact and easy to interpret. The results show that generation of a simple but effective heuristic is possible. Secondly we develop two different types of hyper-heuristic and compare their performance across different combinatorial optimisation problem domains. We construct and compare simplified versions of two existing hyper-heuristics (adaptive and grammar-based), and analyse how each handles the trade-off between computation speed and quality of the solution. The performance of the two hyper-heuristics are tested on seven different problem domains compatible with the HyFlex (Hyper-heuristic Flexible) framework. The results indicate that the adaptive hyper-heuristic is able to deliver solutions of a pre-defined quality in a shorter computational time than the grammar-based hyper-heuristic. Thirdly we investigate how the adaptive hyper-heuristic developed in the second stage of this thesis can respond to problem instances of the same size, but containing different features and complexity. We investigate how, with minimal knowledge about the problem domain and features of the instance being worked on, a hyper-heuristic can modify its processes to respond to problem instances containing different features and problem domains of different complexity. In this stage we allow the adaptive hyper-heuristic to select alternative vectors for the selection of problem domain operators, and acceptance criteria used to determine whether solutions should be retained or discarded. We identify a consistent difference between the best performing pairings of selection vector and acceptance criteria, and those pairings which perform poorly. This thesis shows that hyper-heuristics can respond to scalability issues, although not all do so with equal ease. The flexibility of an adaptive hyper-heuristic enables it to perform faster than the more rigid grammar-based hyper-heuristic, but at the expense of losing a reusable heuristic.</p>

Organisasi Cacat Pejuang Kemerdekaan Indonesia: Dari Ikatan Invaliden Indonesia Hingga Korps Cacad Veteran (1946-1983)

Abstrak: Perang Kemerdekaan Indonesia yang terjadi dalam kurun waktu 1946-1949 menyebabkan banyaknya korban perang yang menjadi cacat. Penelitian ini akan membahas mengenai bagaimana perjuangan selanjutnya para prajurit yang menjadi cacat akibat perang Kemerdekaan Indonesia (1946-1983). Metode penelitian yang digunakan yaitu metode historis yang terdiri dari heuristik, kritik sumber, interpretasi dan historiografi. Penelitian ini bertujuan mendeskripsikan pembentukan dan perkembangan organisasi cacat pejuang kemerdekaan Indonesia dalam usahanya untuk terus berguna bagi negara serta menyejahterakan kehidupan para anggotanya dalam kurun waktu 1946- 1983. Penelitian ini menggunakan metode penelitian sejarah dengan tahapan penelitian heuristik, kritik, interpretasi dan historiografi. Hasil penelitian ini menunjukkan berdirinya organisasi cacat pejuang kemerdekaan pada 1946 di Malang dengan nama Ikatan Invaliden Indonesia dilatarbelakangi oleh adanya upaya untuk memberdayakan dan menyejahterakan korban cacat perang kemerdekaan dan dalam perkembangannya hingga 1983 menunjukkan bahwa organisasi ini memberikan sumbangsih bagi anggotanya yaitu mendapatkan kesejahteraan serta memberikan kesempatan untuk terus berguna bagi negara.Kata Kunci: Invaliden, Korps Cacat Veteran Republik Indonesia, Cacat Veteran.Abstrak: The Indonesian War of Independence that occurred in the period 1946-1949 caused many war victims to become disabled. This research will discusses how the next struggle of the soldiers who became disabled as a result of the war of Indonesian Independence (1946-1983). This study aims to describe the formation and development of disabled organizations for Indonesian independence fighters in their efforts to continue to be useful to the country and to improve the lives of their members in the period 1946-1983. This study uses historical research methods with the stages of heuristic research, criticism, interpretation and historiography. The results of this study indicate that the establishment of a disabled organization for freedom fighters in 1946 in Malang under the name of the Indonesian Invaliden Association was motivated by efforts to empower and prosper the disabled victims of the war of independence and in its development until 1983 showed that this organization contributed to its members, namely getting welfare and providing opportunities for continue to be useful to the country.Keywords: Invalid, Indonesian Republic Veteran Disability Corps, Veteran Disability.

A modified choice function hyper-heuristic with Boltzmann function

Hyper-heuristics are a subclass of high-level research methods that function in a low-level heuristic research space. Their aim objective is to improve the level of generality for solving combinatorial optimization problems using two main components: a methodology for the heuristic selection and a move acceptance criterion, to ensure intensification and diversification [1]. Thus, rather than working directly on the problem's solutions and selecting one of them to proceed to the next step at each stage, hyper-heuristics operates on a low-level heuristic research space. The choice function is one of the hyper-heuristics that have proven their efficiency in solving combinatorial optimization problems [2–4]. At each iteration, the selection of heuristics is dependent on a score calculated by combining three different measures to guarantee both intensification and diversification for the heuristics choice process. The heuristic with the highest score is therefore chosen to be applied to the problem. Therefore, the key to the success of the choice function is to choose the correct weight parameters of its three measures. In this study, we make a state of the art in hyper-heuristic research and propose a new method that automatically controls these weight parameters based on the Boltzmann function. The results obtained from its application on five problem domains are compared with those of the standard, modified choice function proposed by Drake et al. [2,3].

Facing the future

This study aimed to explore the experience of the transpersonal in contemplating retirement, as it impacted on five female co-researchers and myself. All were aged between 55 and 65 and were actively, or recently, engaged in careers in either psychotherapy or therapeutic social work. To allow for deep self-exploration, a heuristic research method, as described by Moustakas (1990), was chosen. Data was collected from my reflective, creative journal, and from the co-researchers using dialogue-based interviews. Data was analysed using an adapted form of thematic analysis. Individual and composite depictions were created and a creative synthesis developed. Two major transpersonal themes emerged: confronting mortality and seeking authenticity and growth. Findings suggest we cannot have the second without acknowledging the first. Subordinate themes demonstrate how we are managing this dilemma and exploring a place for ourselves in the future. Findings further suggest we do this by holding two distinct concepts of time: a linear concept relating to confronting mortality, and an expansive concept relating to seeking meaning and fulfilment in exploring new avenues or rediscovering latent parts of ourselves. These two concepts acknowledge spiritual dimensions in our lives and help us manage the knowledge of mortality. The findings are critically discussed in relation to relevant literature. Finally, the limitations of this study are explored and ideas for future research identified.

Self-Discovery

Heuristic, autoethnographic, or other biographical approaches to doctoral research allow for a deeper understanding of self in context of a phenomenon experienced by the self-as-subject and the greater understanding of others, society, and culture. This chapter presents current research insights into data collection processes used for self-as-subject research at the doctoral level. Illustrations of the variety of data sources used for both heuristic research and autoethnography are presented as well as insights and recommendations from method experts are included.