37,140 research outputs found
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model
Multi-agent geographical models integrate very large numbers of spatial
interactions. In order to validate those models large amount of computing is
necessary for their simulation and calibration. Here a new data processing
chain including an automated calibration procedure is experimented on a
computational grid using evolutionary algorithms. This is applied for the first
time to a geographical model designed to simulate the evolution of an early
urban settlement system. The method enables us to reduce the computing time and
provides robust results. Using this method, we identify several parameter
settings that minimise three objective functions that quantify how closely the
model results match a reference pattern. As the values of each parameter in
different settings are very close, this estimation considerably reduces the
initial possible domain of variation of the parameters. The model is thus a
useful tool for further multiple applications on empirical historical
situations
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
Embodied Evolution in Collective Robotics: A Review
This paper provides an overview of evolutionary robotics techniques applied
to on-line distributed evolution for robot collectives -- namely, embodied
evolution. It provides a definition of embodied evolution as well as a thorough
description of the underlying concepts and mechanisms. The paper also presents
a comprehensive summary of research published in the field since its inception
(1999-2017), providing various perspectives to identify the major trends. In
particular, we identify a shift from considering embodied evolution as a
parallel search method within small robot collectives (fewer than 10 robots) to
embodied evolution as an on-line distributed learning method for designing
collective behaviours in swarm-like collectives. The paper concludes with a
discussion of applications and open questions, providing a milestone for past
and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl
Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids
Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively)
Where creativity comes from: the social spaces of embodied minds
This paper explores creative design, social interaction and perception. It proposes that creativity at a social level is not a result of many individuals trying to be creative at a personal level, but occurs naturally in the social interaction between comparatively simple minds embodied in a complex world. Particle swarm algorithms can model group interaction in shared spaces, but design space is not necessarily one pre-defined space of set parameters on which everyone can agree, as individual minds are very different. A computational model is proposed that allows a similar swarm to occur between spaces of different description and even dimensionality. This paper explores creative design, social interaction and perception. It proposes that creativity at a social level is not a result of many individuals trying to be creative at a personal level, but occurs naturally in the social interaction between comparatively simple minds embodied in a complex world. Particle swarm algorithms can model group interaction in shared spaces, but design space is not necessarily one pre-defined space of set parameters on which everyone can agree, as individual minds are very different. A computational model is proposed that allows a similar swarm to occur between spaces of different description and even dimensionality
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