57 research outputs found
-ilities Tradespace and Affordability Project – Phase 3
One of the key elements of the SERC’s research strategy is transforming the practice of systems engineering and associated management practices – “SE and Management Transformation (SEMT).” The Grand Challenge goal for SEMT is to transform the DoD community’s current systems engineering and management methods, processes, and tools (MPTs) and practices away from sequential, single stovepipe system, hardware-first, document-driven, point- solution, acquisition-oriented approaches; and toward concurrent, portfolio and enterprise- oriented, hardware-software-human engineered, model-driven, set-based, full life cycle approaches.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046).This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046)
Competent Program Evolution, Doctoral Dissertation, December 2006
Heuristic optimization methods are adaptive when they sample problem solutions based on knowledge of the search space gathered from past sampling. Recently, competent evolutionary optimization methods have been developed that adapt via probabilistic modeling of the search space. However, their effectiveness requires the existence of a compact problem decomposition in terms of prespecified solution parameters. How can we use these techniques to effectively and reliably solve program learning problems, given that program spaces will rarely have compact decompositions? One method is to manually build a problem-specific representation that is more tractable than the general space. But can this process be automated? My thesis is that the properties of programs and program spaces can be leveraged as inductive bias to reduce the burden of manual representation-building, leading to competent program evolution. The central contributions of this dissertation are a synthesis of the requirements for competent program evolution, and the design of a procedure, meta-optimizing semantic evolutionary search (MOSES), that meets these requirements. In support of my thesis, experimental results are provided to analyze and verify the effectiveness of MOSES, demonstrating scalability and real-world applicability
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Automatic design of analogue circuits
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Evolvable Hardware (EHW) is a promising area in electronics today. Evolutionary Algorithms (EA), together with a circuit simulation tool or real hardware, automatically designs a circuit for a given problem. The circuits evolved may have unconventional designs and be less dependent on the personal knowledge of a designer. Nowadays, EA are represented by Genetic Algorithms (GA), Genetic Programming (GP) and Evolutionary Strategy (ES). While GA is definitely the most popular tool, GP has rapidly developed in recent years and is notable by its outstanding results. However, to date the use of ES for analogue circuit synthesis has been limited to a few applications.
This work is devoted to exploring the potential of ES to create novel analogue designs. The narrative of the thesis starts with a framework of an ES-based system generating simple circuits, such as low pass filters. Then it continues with a step-by-step progression to increasingly sophisticated designs that require additional strength from the system. Finally, it describes the modernization of the system using novel techniques that enable the synthesis of complex multi-pin circuits that are newly evolved.
It has been discovered that ES has strong power to synthesize analogue circuits. The circuits evolved in the first part of the thesis exceed similar results made previously using other techniques in a component economy, in the better functioning of the evolved circuits and in the computing power spent to reach the results. The target circuits for evolution in the second half are chosen by the author to challenge the capability of the developed system. By functioning, they do not belong to the conventional analogue domain but to applications that are usually adopted by digital circuits. To solve the design tasks, the system has been gradually developed to support the ability of evolving increasingly complex circuits.
As a final result, a state-of-the-art ES-based system has been developed that possesses a novel mutation paradigm, with an ability to create, store and reuse substructures, to adapt the mutation, selection parameters and population size, utilize automatic incremental evolution and use the power of parallel computing. It has been discovered that with the ability to synthesis the most up-to-date multi-pin complex analogue circuits that have ever been automatically synthesized before, the system is capable of synthesizing circuits that are problematic for conventional design with application domains that lay beyond the conventional application domain for analogue circuits
A Multi-Level Framework for the Detection, Prioritization and Testing of Software Design Defects
Large-scale software systems exhibit high complexity and become difficult to maintain. In fact, it has been reported that software cost dedicated to maintenance and evolution activities is more
than 80% of the total software costs. In particular, object-oriented software systems need to
follow some traditional design principles such as data abstraction, encapsulation, and modularity.
However, some of these non-functional requirements can be violated by developers for many
reasons such as inexperience with object-oriented design principles, deadline stress. This high
cost of maintenance activities could potentially be greatly reduced by providing automatic or
semi-automatic solutions to increase system‟s comprehensibility, adaptability and extensibility to
avoid bad-practices.
The detection of refactoring opportunities focuses on the detection of bad smells, also called
antipatterns, which have been recognized as the design situations that may cause software
failures indirectly. The correction of one bad smell may influence other bad smells. Thus, the
order of fixing bad smells is important to reduce the effort and maximize the refactoring benefits.
However, very few studies addressed the problem of finding the optimal sequence in which the
refactoring opportunities, such as bad smells, should be ordered. Few other studies tried to
prioritize refactoring opportunities based on the types of bad smells to determine their severity.
However, the correction of severe bad smells may require a high effort which should be
optimized and the relationships between the different bad smells are not considered during the
prioritization process.
The main goal of this research is to help software engineers to refactor large-scale systems with a
minimum effort and few interactions including the detection, management and testing of
refactoring opportunities. We report the results of an empirical study with an implementation of
our bi-level approach. The obtained results provide evidence to support the claim that our
proposal is more efficient, on average, than existing techniques based on a benchmark of 9 open
source systems and 1 industrial project. We have also evaluated the relevance and usefulness of
the proposed bi-level framework for software engineers to improve the quality of their systems
and support the detection of transformation errors by generating efficient test cases.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136075/1/Dilan_Sahin_Final Dissertation.pdfDescription of Dilan_Sahin_Final Dissertation.pdf : Dissertatio
Engineering Automation for Reliable Software Interim Progress Report (10/01/2000 - 09/30/2001)
Prepared for: U.S. Army Research Office
P.O. Box 12211
Research Triangle Park, NC 27709-2211The objective of our effort is to develop a scientific basis for producing reliable
software that is also flexible and cost effective for the DoD distributed software domain.
This objective addresses the long term goals of increasing the quality of service provided
by complex systems while reducing development risks, costs, and time. Our work focuses on
"wrap and glue" technology based on a domain specific distributed prototype model. The key
to making the proposed approach reliable, flexible, and cost-effective is the automatic
generation of glue and wrappers based on a designer's specification. The "wrap and glue"
approach allows system designers to concentrate on the difficult interoperability problems
and defines solutions in terms of deeper and more difficult interoperability issues, while
freeing designers from implementation details. Specific research areas for the proposed
effort include technology enabling rapid prototyping, inference for design checking,
automatic program generation, distributed real-time scheduling, wrapper and glue
technology, and reliability assessment and improvement. The proposed technology will be
integrated with past research results to enable a quantum leap forward in the state of the
art for rapid prototyping.U. S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-22110473-MA-SPApproved for public release; distribution is unlimited
Ontology evolution in physics
With the advent of reasoning problems in dynamic environments, there is an increasing
need for automated reasoning systems to automatically adapt to unexpected changes
in representations. In particular, the automation of the evolution of their ontologies
needs to be enhanced without substantially sacrificing expressivity in the underlying
representation. Revision of beliefs is not enough, as adding to or removing from beliefs
does not change the underlying formal language. General reasoning systems employed
in such environments should also address situations in which the language for representing
knowledge is not shared among the involved entities, e.g., the ontologies in
a multi-ontology environment or the agents in a multi-agent environment. Our techniques
involve diagnosis of faults in existing, possibly heterogeneous, ontologies and
then resolution of these faults by manipulating the signature and/or the axioms.
This thesis describes the design, development and evaluation of GALILEO (Guided
Analysis of Logical Inconsistencies Lead to Evolution of Ontologies), a system designed
to detect conflicts in highly expressive ontologies and resolve the detected conflicts
by performing appropriate repair operations. The integrated mechanism that
handles ontology evolution is able to distinguish between various types of conflicts,
each corresponding to a unique kind of ontological fault. We apply and develop our
techniques in the domain of Physics. This an excellent domain because many of its
seminal advances can be seen as examples of ontology evolution, i.e. changing the
way that physicists perceive the world, and case studies are well documented – unlike
many other domains. Our research covers analysing a wide ranging development set
of case studies and evaluating the performance of the system on a test set. Because
the formal representations of most of the case studies are non-trivial and the underlying
logic has a high degree of expressivity, we face some tricky technical challenges,
including dealing with the potentially large number of choices in diagnosis and repair.
In order to enhance the practicality and the manageability of the ontology evolution
process, GALILEO incorporates the functionality of generating physically meaningful
diagnoses and repairs and, as a result, narrowing the search space to a manageable size
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