9,735 research outputs found
French Roadmap for complex Systems 2008-2009
This second issue of the French Complex Systems Roadmap is the outcome of the
Entretiens de Cargese 2008, an interdisciplinary brainstorming session
organized over one week in 2008, jointly by RNSC, ISC-PIF and IXXI. It
capitalizes on the first roadmap and gathers contributions of more than 70
scientists from major French institutions. The aim of this roadmap is to foster
the coordination of the complex systems community on focused topics and
questions, as well as to present contributions and challenges in the complex
systems sciences and complexity science to the public, political and industrial
spheres
AN INVESTIGATION OF EVOLUTIONARY COMPUTING IN SYSTEMS IDENTIFICATION FOR PRELIMINARY DESIGN
This research investigates the integration of evolutionary techniques for symbolic
regression. In particular the genetic programming paradigm is used together with other
evolutionary computational techniques to develop novel approaches to the improvement of
areas of simple preliminary design software using empirical data sets. It is shown that within
this problem domain, conventional genetic programming suffers from several limitations,
which are overcome by the introduction of an improved genetic programming strategy
based on node complexity values, and utilising a steady state algorithm with subpopulations.
A further extension to the new technique is introduced which incorporates a
genetic algorithm to aid the search within continuous problem spaces, increasing the
robustness of the new method. The work presented here represents an advance in the Geld
of genetic programming for symbolic regression with significant improvements over the
conventional genetic programming approach. Such improvement is illustrated by extensive
experimentation utilising both simple test functions and real-world design examples
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
Geometric generalisation of surrogate model-based optimisation to combinatorial and program spaces
Open access journalSurrogate models (SMs) can profitably be employed, often in conjunction with evolutionary algorithms, in optimisation in which it is expensive to test candidate solutions. The spatial intuition behind SMs makes them naturally suited to continuous problems, and the only combinatorial problems that have been previously addressed are those with solutions that can be encoded as integer vectors. We show how radial basis functions can provide a generalised SM for combinatorial problems which have a geometric solution representation, through the conversion of that representation to a different metric space. This approach allows an SM to be cast in a natural way for the problem at hand, without ad hoc adaptation to a specific representation. We test this adaptation process on problems involving binary strings, permutations, and tree-based genetic programs. © 2014 Yong-Hyuk Kim et al
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