16,249 research outputs found
Finding a boundary between valid and invalid regions of the input space
In the context of robustness testing, the boundary between the valid and
invalid regions of the input space can be an interesting source of erroneous
inputs. Knowing where a specific software under test (SUT) has a boundary is
essential for validation in relation to requirements. However, finding where a
SUT actually implements the boundary is a non-trivial problem that has not
gotten much attention. This paper proposes a method of finding the boundary
between the valid and invalid regions of the input space. The proposed method
consists of two steps. First, test data generators, directed by a search
algorithm to maximise distance to known, valid test cases, generate valid test
cases that are closer to the boundary. Second, these valid test cases undergo
mutations to try to push them over the boundary and into the invalid part of
the input space. This results in a pair of test sets, one consisting of test
cases on the valid side of the boundary and a matched set on the outer side,
with only a small distance between the two sets. The method is evaluated on a
number of examples from the standard library of a modern programming language.
We propose a method of determining the boundary between valid and invalid
regions of the input space and apply it on a SUT that has a non-contiguous
valid region of the input space. From the small distance between the developed
pairs of test sets, and the fact that one test set contains valid test cases
and the other invalid test cases, we conclude that the pair of test sets
described the boundary between the valid and invalid regions of that input
space. Differences of behaviour can be observed between different distances and
sets of mutation operators, but all show that the method is able to identify
the boundary between the valid and invalid regions of the input space. This is
an important step towards more automated robustness testing.Comment: 10 pages, conferenc
Automated Generation of Cross-Domain Analogies via Evolutionary Computation
Analogy plays an important role in creativity, and is extensively used in
science as well as art. In this paper we introduce a technique for the
automated generation of cross-domain analogies based on a novel evolutionary
algorithm (EA). Unlike existing work in computational analogy-making restricted
to creating analogies between two given cases, our approach, for a given case,
is capable of creating an analogy along with the novel analogous case itself.
Our algorithm is based on the concept of "memes", which are units of culture,
or knowledge, undergoing variation and selection under a fitness measure, and
represents evolving pieces of knowledge as semantic networks. Using a fitness
function based on Gentner's structure mapping theory of analogies, we
demonstrate the feasibility of spontaneously generating semantic networks that
are analogous to a given base network.Comment: Conference submission, International Conference on Computational
Creativity 2012 (8 pages, 6 figures
A genetic algorithm-assisted semi-adaptive MMSE multi-user detection for MC-CDMA mobile communication systems
In this work, a novel Minimum-Mean Squared-Error (MMSE) multi-user detector is proposed for MC-CDMA transmission systems working over mobile radio channels characterized by time-varying multipath fading. The proposed MUD algorithm is based on a Genetic Algorithm (GA)-assisted per-carrier MMSE criterion. The GA block works in two successive steps: a training-aided step aimed at computing the optimal receiver weights using a very short training sequence, and a decision-directed step aimed at dynamically updating the weights vector during a channel coherence period. Numerical results evidenced BER performances almost coincident with ones yielded by ideal MMSE-MUD based on the perfect knowledge of channel impulse response. The proposed GA-assisted MMSE-MUD clearly outperforms state-of-the-art adaptive MMSE receivers based on deterministic gradient algorithms, especially for high number of transmitting users
Representing Space: A Hybrid Genetic Algorithm for Aesthetic Graph Layout
This paper describes a hybrid Genetic Algorithm (GA) that is used to improve the layout of a graph according to a number of aesthetic criteria. The GA incorporates spatial and topological information by operating directly with a graph based representation. Initial results show this to be a promising technique for positioning graph nodes on a surface and may form the basis of a more general approach for problems involving multi-criteria spatial optimisation
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file
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