78,746 research outputs found
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture Search
Evolutionary algorithms (EAs) have gained attention recently due to their
success in neural architecture search (NAS). However, whereas traditional EAs
draw much power from crossover operations, most evolutionary NAS methods deploy
only mutation operators. The main reason is the permutation problem: The
mapping between genotype and phenotype in traditional graph representations is
many-to-one, leading to a disruptive effect of standard crossover. This work
conducts the first theoretical analysis of the behaviors of crossover and
mutation in the NAS context, and proposes a new crossover operator based on the
shortest edit path (SEP) in graph space. The SEP crossover is shown to overcome
the permutation problem, and as a result, offspring generated by the SEP
crossover is theoretically proved to have a better expected improvement in
terms of graph edit distance to global optimum, compared to mutation and
standard crossover. Experiments further show that the SEP crossover
significantly outperforms mutation and standard crossover on three
state-of-the-art NAS benchmarks. The SEP crossover therefore allows taking full
advantage of evolution in NAS, and potentially other similar design problems as
well.Comment: 17 pages, 6 figure
Evolved Art with Transparent, Overlapping, and Geometric Shapes
In this work, an evolutionary art project is presented where images are
approximated by transparent, overlapping and geometric shapes of different
types, e.g., polygons, circles, lines. Genotypes representing features and
order of the geometric shapes are evolved with a fitness function that has the
corresponding pixels of an input image as a target goal. A
genotype-to-phenotype mapping is therefore applied to render images, as the
chosen genetic representation is indirect, i.e., genotypes do not include
pixels but a combination of shapes with their properties. Different
combinations of shapes, quantity of shapes, mutation types and populations are
tested. The goal of the work herein is twofold: (1) to approximate images as
precisely as possible with evolved indirect encodings, (2) to produce visually
appealing results and novel artistic styles.Comment: Proceedings of the Norwegian AI Symposium 2019 (NAIS 2019),
Trondheim, Norwa
Finding Near-Optimal Independent Sets at Scale
The independent set problem is NP-hard and particularly difficult to solve in
large sparse graphs. In this work, we develop an advanced evolutionary
algorithm, which incorporates kernelization techniques to compute large
independent sets in huge sparse networks. A recent exact algorithm has shown
that large networks can be solved exactly by employing a branch-and-reduce
technique that recursively kernelizes the graph and performs branching.
However, one major drawback of their algorithm is that, for huge graphs,
branching still can take exponential time. To avoid this problem, we
recursively choose vertices that are likely to be in a large independent set
(using an evolutionary approach), then further kernelize the graph. We show
that identifying and removing vertices likely to be in large independent sets
opens up the reduction space---which not only speeds up the computation of
large independent sets drastically, but also enables us to compute high-quality
independent sets on much larger instances than previously reported in the
literature.Comment: 17 pages, 1 figure, 8 tables. arXiv admin note: text overlap with
arXiv:1502.0168
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
Memetic Multilevel Hypergraph Partitioning
Hypergraph partitioning has a wide range of important applications such as
VLSI design or scientific computing. With focus on solution quality, we develop
the first multilevel memetic algorithm to tackle the problem. Key components of
our contribution are new effective multilevel recombination and mutation
operations that provide a large amount of diversity. We perform a wide range of
experiments on a benchmark set containing instances from application areas such
VLSI, SAT solving, social networks, and scientific computing. Compared to the
state-of-the-art hypergraph partitioning tools hMetis, PaToH, and KaHyPar, our
new algorithm computes the best result on almost all instances
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