1,765 research outputs found
Negatively Correlated Search
Evolutionary Algorithms (EAs) have been shown to be powerful tools for
complex optimization problems, which are ubiquitous in both communication and
big data analytics. This paper presents a new EA, namely Negatively Correlated
Search (NCS), which maintains multiple individual search processes in parallel
and models the search behaviors of individual search processes as probability
distributions. NCS explicitly promotes negatively correlated search behaviors
by encouraging differences among the probability distributions (search
behaviors). By this means, individual search processes share information and
cooperate with each other to search diverse regions of a search space, which
makes NCS a promising method for non-convex optimization. The cooperation
scheme of NCS could also be regarded as a novel diversity preservation scheme
that, different from other existing schemes, directly promotes diversity at the
level of search behaviors rather than merely trying to maintain diversity among
candidate solutions. Empirical studies showed that NCS is competitive to
well-established search methods in the sense that NCS achieved the best overall
performance on 20 multimodal (non-convex) continuous optimization problems. The
advantages of NCS over state-of-the-art approaches are also demonstrated with a
case study on the synthesis of unequally spaced linear antenna arrays
Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives
Collectiveness is an important property of many systems--both natural and
artificial. By exploiting a large number of individuals, it is often possible
to produce effects that go far beyond the capabilities of the smartest
individuals, or even to produce intelligent collective behaviour out of
not-so-intelligent individuals. Indeed, collective intelligence, namely the
capability of a group to act collectively in a seemingly intelligent way, is
increasingly often a design goal of engineered computational systems--motivated
by recent techno-scientific trends like the Internet of Things, swarm robotics,
and crowd computing, just to name a few. For several years, the collective
intelligence observed in natural and artificial systems has served as a source
of inspiration for engineering ideas, models, and mechanisms. Today, artificial
and computational collective intelligence are recognised research topics,
spanning various techniques, kinds of target systems, and application domains.
However, there is still a lot of fragmentation in the research panorama of the
topic within computer science, and the verticality of most communities and
contributions makes it difficult to extract the core underlying ideas and
frames of reference. The challenge is to identify, place in a common structure,
and ultimately connect the different areas and methods addressing intelligent
collectives. To address this gap, this paper considers a set of broad scoping
questions providing a map of collective intelligence research, mostly by the
point of view of computer scientists and engineers. Accordingly, it covers
preliminary notions, fundamental concepts, and the main research perspectives,
identifying opportunities and challenges for researchers on artificial and
computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for
publication in the Artificial Life journal. Data: 34 pages, 2 figure
Neural Architecture Search by Estimation of Network Structure Distributions
The influence of deep learning is continuously expanding across different
domains, and its new applications are ubiquitous. The question of neural
network design thus increases in importance, as traditional empirical
approaches are reaching their limits. Manual design of network architectures
from scratch relies heavily on trial and error, while using existing pretrained
models can introduce redundancies or vulnerabilities. Automated neural
architecture design is able to overcome these problems, but the most successful
algorithms operate on significantly constrained design spaces, assuming the
target network to consist of identical repeating blocks. While such approach
allows for faster search, it does so at the cost of expressivity. We instead
propose an alternative probabilistic representation of a whole neural network
structure under the assumption of independence between layer types. Our matrix
of probabilities is equivalent to the population of models, but allows for
discovery of structural irregularities, while being simple to interpret and
analyze. We construct an architecture search algorithm, inspired by the
estimation of distribution algorithms, to take advantage of this
representation. The probability matrix is tuned towards generating
high-performance models by repeatedly sampling the architectures and evaluating
the corresponding networks, while gradually increasing the model depth. Our
algorithm is shown to discover non-regular models which cannot be expressed via
blocks, but are competitive both in accuracy and computational cost, while not
utilizing complex dataflows or advanced training techniques, as well as
remaining conceptually simple and highly extensible.Comment: 16 pages, 4 figures, 3 table
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