7,434 research outputs found
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
"Going back to our roots": second generation biocomputing
Researchers in the field of biocomputing have, for many years, successfully
"harvested and exploited" the natural world for inspiration in developing
systems that are robust, adaptable and capable of generating novel and even
"creative" solutions to human-defined problems. However, in this position paper
we argue that the time has now come for a reassessment of how we exploit
biology to generate new computational systems. Previous solutions (the "first
generation" of biocomputing techniques), whilst reasonably effective, are crude
analogues of actual biological systems. We believe that a new, inherently
inter-disciplinary approach is needed for the development of the emerging
"second generation" of bio-inspired methods. This new modus operandi will
require much closer interaction between the engineering and life sciences
communities, as well as a bidirectional flow of concepts, applications and
expertise. We support our argument by examining, in this new light, three
existing areas of biocomputing (genetic programming, artificial immune systems
and evolvable hardware), as well as an emerging area (natural genetic
engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin
Synthesis of time-to-amplitude converter by mean coevolution with adaptive parameters
Copyright © 2011 the authors and Scientific Research Publishing Inc. This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)The challenging task to synthesize automatically a time-to-amplitude converter, which unites by its functionality several digital circuits, has been successfully solved with the help of a novel methodology. The proposed approach is based on a paradigm according to which the substructures are regarded as additional mutation types and when ranged with other mutations form a new adaptive individual-level mutation technique. This mutation approach led to the discovery of an original coevolution strategy that is characterized by very low selection rates. Parallel island-model evolution has been running in a hybrid competitive-cooperative interaction throughout two incremental stages. The adaptive population size is applied for synchronization of the parallel evolutions
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
Recommended from our members
Methodology for identifying alternative solutions in a population based data generation approach applied to synthetic biology
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDesign is an essential component of sustainable development. Computational modelling has
become a useful technique that facilitates the design of complex systems. Variables that characterises
a complex system are encoded into a computational model using mathematical concepts
and through simulation each of these variables alone or in combination are modified to observe
the changes in the outcome. This allows the researchers to make predictions on the behaviour
of the real system that is being studied in response to the changes. The ultimate goal of any
design process is to come up with the best design; as resources are limited, to minimize the cost
and resource consumption, and to maximize the performance, profits and efficiency. To optimize
means to find the best solution, the best compromise among several conflicting demands subject
to predefined requirements. Therefore, computational optimization, modelling and simulation
forms an integrated part of the modern design practice.
This thesis defines a data analytics driven methodology which enables the identification of
alternative solutions of computational design by analysing the generational history of the population
based heuristic search used to generate the templates. While optimisation is focused on
obtaining the optimal solution this methodology focuses on alternative solutions which are sub
optimal by fitness or solutions with similar fitness but different structures. When the optimal
design solution is less robust, alternative solutions can offer a sufficiently good accuracy and an
achievable resource requirement. The main advantage of the methodology is that it exploits the
exploration process of the solution space during a single run, by focusing also on suboptimal
solutions, which usually get neglected in the search for an optimal one. The history of the
heuristic search is analysed for the emergence of alternative solutions and evolving of a solution.
By examining how an initial solution converts to an optimal solution core design patterns are
identified, and these were used to improve the design process. Further, this method limits the
number of runs of the heuristic search as more solution space is covered. The methodology is
generic because it can be used to any instance where a population based heuristic search is applied
to generate optimal designs. The applicability of the methodology is demonstrated using
three case studies from mathematics (building of a mathematical function for a set target) and
biology (obtaining alternative designs for genomic metabolic models [GEM] and DNA walker
circuits). In each case a different heuristic search method was used: Gene expression programming
(mathematical expressions), genetic algorithms (GEM models) and simulated annealing
(DNA walker circuits). Descriptive analytics, visual analytics and clustering was mainly used to build the data analytics driven approach in identifying alternative solutions. This data analytics
driven methodology is useful in optimising the computational design of complex systems
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