1,765 research outputs found
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to
predict real valued time-series. The method consists in projecting the output
of the internal layer of the network on a space with lower dimensionality,
before training the output layer to learn the target task. Notably, we enforce
a regularization constraint that leads to better generalization capabilities.
We evaluate the performances of our approach on several benchmark tests, using
different techniques to train the readout of the network, achieving superior
predictive performance when using the proposed framework. Finally, we provide
an insight on the effectiveness of the implemented mechanics through a
visualization of the trajectory in the phase space and relying on the
methodologies of nonlinear time-series analysis. By applying our method on well
known chaotic systems, we provide evidence that the lower dimensional embedding
retains the dynamical properties of the underlying system better than the
full-dimensional internal states of the network
Cultural Algorithm based on Decomposition to solve Optimization Problems
Decomposition is used to solve optimization problems by introducing many simple scalar optimization subproblems and optimizing them simultaneously. Dynamic Multi-Objective Optimization Problems (DMOP) have several objective functions and constraints that vary over time. As a consequence of such dynamic changes, the optimal solutions may vary over time, affecting the performance of convergence. In this thesis, we propose a new Cultural Algorithm (CA) based on decomposition (CA/D). The objective of the CA/D algorithm is to decompose DMOP into a number of subproblems that can be optimized using the information shared by neighboring problems. The proposed CA/D approach is evaluated using a number of CEC 2015 optimization benchmark functions. When compared to CA, Multi-population CA (MPCA), and MPCA incorporating game strategies (MPCA-GS), the results obtained showed that CA/D outperformed them in 7 out of the 15 benchmark functions
COIL: Constrained optimization in learned latent space: learning representations for valid solutions
Constrained optimization problems can be difficult because their search
spaces have properties not conducive to search, e.g., multimodality,
discontinuities, or deception. To address such difficulties, considerable
research has been performed on creating novel evolutionary algorithms or
specialized genetic operators. However, if the representation that defined the
search space could be altered such that it only permitted valid solutions that
satisfied the constraints, the task of finding the optimal would be made more
feasible without any need for specialized optimization algorithms. We propose
Constrained Optimization in Latent Space (COIL), which uses a VAE to generate a
learned latent representation from a dataset comprising samples from the valid
region of the search space according to a constraint, thus enabling the
optimizer to find the objective in the new space defined by the learned
representation. Preliminary experiments show promise: compared to an identical
GA using a standard representation that cannot meet the constraints or find fit
solutions, COIL with its learned latent representation can perfectly satisfy
different types of constraints while finding high-fitness solutions
The Cowl - v.56 - n.17 - Mar 19, 1992
The Cowl - student newspaper of Providence College. Volume 56, Number 17 - March 19, 1992. 24 pages
Evolutionary games on graphs
Game theory is one of the key paradigms behind many scientific disciplines
from biology to behavioral sciences to economics. In its evolutionary form and
especially when the interacting agents are linked in a specific social network
the underlying solution concepts and methods are very similar to those applied
in non-equilibrium statistical physics. This review gives a tutorial-type
overview of the field for physicists. The first three sections introduce the
necessary background in classical and evolutionary game theory from the basic
definitions to the most important results. The fourth section surveys the
topological complications implied by non-mean-field-type social network
structures in general. The last three sections discuss in detail the dynamic
behavior of three prominent classes of models: the Prisoner's Dilemma, the
Rock-Scissors-Paper game, and Competing Associations. The major theme of the
review is in what sense and how the graph structure of interactions can modify
and enrich the picture of long term behavioral patterns emerging in
evolutionary games.Comment: Review, final version, 133 pages, 65 figure
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