14,484 research outputs found
Improving EHW performance introducing a new decomposition strategy
This paper describes a new type of decomposition strategy for Evolvable Hardware, which tackles the problem of scalability. Several logic circuits from the MCNC benchmark have been evolved and compared with other Evolvable Hardware techniques. The results demonstrate that the proposed method improves the evolution of logic circuits in terms of time and fitness function in comparison with BIE and standard EHW
Genetic algorithms
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology
The Algorithmic Origins of Life
Although it has been notoriously difficult to pin down precisely what it is
that makes life so distinctive and remarkable, there is general agreement that
its informational aspect is one key property, perhaps the key property. The
unique informational narrative of living systems suggests that life may be
characterized by context-dependent causal influences, and in particular, that
top-down (or downward) causation -- where higher-levels influence and constrain
the dynamics of lower-levels in organizational hierarchies -- may be a major
contributor to the hierarchal structure of living systems. Here we propose that
the origin of life may correspond to a physical transition associated with a
shift in causal structure, where information gains direct, and
context-dependent causal efficacy over the matter it is instantiated in. Such a
transition may be akin to more traditional physical transitions (e.g.
thermodynamic phase transitions), with the crucial distinction that determining
which phase (non-life or life) a given system is in requires dynamical
information and therefore can only be inferred by identifying causal
architecture. We discuss some potential novel research directions based on this
hypothesis, including potential measures of such a transition that may be
amenable to laboratory study, and how the proposed mechanism corresponds to the
onset of the unique mode of (algorithmic) information processing characteristic
of living systems.Comment: 13 pages, 1 tabl
Open-ended evolution to discover analogue circuits for beyond conventional applications
This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s10710-012-9163-8. Copyright @ Springer 2012.Analogue circuits synthesised by means of open-ended evolutionary algorithms often have unconventional designs. However, these circuits are typically highly compact, and the general nature of the evolutionary search methodology allows such designs to be used in many applications. Previous work on the evolutionary design of analogue circuits has focused on circuits that lie well within analogue application domain. In contrast, our paper considers the evolution of analogue circuits that are usually synthesised in digital logic. We have developed four computational circuits, two voltage distributor circuits and a time interval metre circuit. The approach, despite its simplicity, succeeds over the design tasks owing to the employment of substructure reuse and incremental evolution. Our findings expand the range of applications that are considered suitable for evolutionary electronics
Network-based business process management: embedding business logic in communications networks
Advanced Business Process Management (BPM) tools enable the decomposition of previously integrated and often ill-defined processes into re-usable process modules. These process modules can subsequently be distributed on the Internet over a variety of many different actors, each with their own specialization and economies-of-scale. The economic benefits of process specialization can be huge. However, how should such actors in a business network find, select, and control, the best partner for what part of the business process, in such a way that the best result is achieved? This particular management challenge requires more advanced techniques and tools in the enabling communications networks. An approach has been developed to embed business logic into the communications networks in order to optimize the allocation of business resources from a network point of view. Initial experimental results have been encouraging while at the same time demonstrating the need for more robust techniques in a future of massively distributed business processes.active networks;business process management;business protocols;embedded business logic;genetic algorithms;internet distributed process management;payment systems;programmable networks;resource optimization
Issues in the Scalability of Gate-level Morphogenetic Evolvable Hardware
Traditional approaches to evolvable hardware (EHW), in which the field programmable gate array (FPGA) configuration is directly encoded, have not scaled well with increasing circuit and FPGA complexity. To overcome this there have been moves towards encoding a growth process, known as morphogenesis. Using a morphogenetic approach, has shown success in scaling gate-level EHW for a signal routing problem, however, when faced with a evolving a one-bit full adder, unforseen difficulties were encountered. In this paper, we provide a measurement of EHW problem difficulty that takes into account the salient features of the problem, and when combined with a measure of feedback from the fitness function, we are able to estimate whether or not a given EHW problem is likely to be able to be solved successfully by our morphogenetic approach. Using these measurements we are also able to give an indication of the scalability of morphogenesis when applied to EHW
Multi-input distributed classifiers for synthetic genetic circuits
For practical construction of complex synthetic genetic networks able to
perform elaborate functions it is important to have a pool of relatively simple
"bio-bricks" with different functionality which can be compounded together. To
complement engineering of very different existing synthetic genetic devices
such as switches, oscillators or logical gates, we propose and develop here a
design of synthetic multiple input distributed classifier with learning
ability. Proposed classifier will be able to separate multi-input data, which
are inseparable for single input classifiers. Additionally, the data classes
could potentially occupy the area of any shape in the space of inputs. We study
two approaches to classification, including hard and soft classification and
confirm the schemes of genetic networks by analytical and numerical results
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