5,085 research outputs found
The conduciveness of CA-rule graphs
Given two subsets A and B of nodes in a directed graph, the conduciveness of
the graph from A to B is the ratio representing how many of the edges outgoing
from nodes in A are incoming to nodes in B. When the graph's nodes stand for
the possible solutions to certain problems of combinatorial optimization,
choosing its edges appropriately has been shown to lead to conduciveness
properties that provide useful insight into the performance of algorithms to
solve those problems. Here we study the conduciveness of CA-rule graphs, that
is, graphs whose node set is the set of all CA rules given a cell's number of
possible states and neighborhood size. We consider several different edge sets
interconnecting these nodes, both deterministic and random ones, and derive
analytical expressions for the resulting graph's conduciveness toward rules
having a fixed number of non-quiescent entries. We demonstrate that one of the
random edge sets, characterized by allowing nodes to be sparsely interconnected
across any Hamming distance between the corresponding rules, has the potential
of providing reasonable conduciveness toward the desired rules. We conjecture
that this may lie at the bottom of the best strategies known to date for
discovering complex rules to solve specific problems, all of an evolutionary
nature
Generalised additive multiscale wavelet models constructed using particle swarm optimisation and mutual information for spatio-temporal evolutionary system representation
A new class of generalised additive multiscale wavelet models (GAMWMs) is introduced for high dimensional spatio-temporal evolutionary (STE) system identification. A novel two-stage hybrid learning scheme is developed for constructing such an additive wavelet model. In the first stage, a new orthogonal projection pursuit (OPP) method, implemented using a particle swarm optimisation(PSO) algorithm, is proposed for successively augmenting an initial coarse wavelet model, where relevant parameters of the associated wavelets are optimised using a particle swarm optimiser. The resultant network model, obtained in the first stage, may however be a redundant model. In the second stage, a forward orthogonal regression (FOR) algorithm, implemented using a mutual information method, is then applied to refine and improve the initially constructed wavelet model. The proposed two-stage hybrid method can generally produce a parsimonious wavelet model, where a ranked list of wavelet functions, according to the capability of each wavelet to represent the total variance in the desired system output signal is produced. The proposed new modelling framework is applied to real observed images, relative to a chemical reaction exhibiting a spatio-temporal evolutionary behaviour, and the associated identification results show that the new modelling framework is applicable and effective for handling high dimensional identification problems of spatio-temporal evolution sytems
Identification of cellular automata based on incomplete observations with bounded time gaps
In this paper, the problem of identifying the cellular automata (CAs) is considered. We frame and solve this problem in the context of incomplete observations, i.e., prerecorded, incomplete configurations of the system at certain, and unknown time stamps. We consider 1-D, deterministic, two-state CAs only. An identification method based on a genetic algorithm with individuals of variable length is proposed. The experimental results show that the proposed method is highly effective. In addition, connections between the dynamical properties of CAs (Lyapunov exponents and behavioral classes) and the performance of the identification algorithm are established and analyzed
An evolutionary approach to the identification of Cellular Automata based on partial observations
In this paper we consider the identification problem of Cellular Automata
(CAs). The problem is defined and solved in the context of partial observations
with time gaps of unknown length, i.e. pre-recorded, partial configurations of
the system at certain, unknown time steps. A solution method based on a
modified variant of a Genetic Algorithm (GA) is proposed and illustrated with
brief experimental results.Comment: IEEE CEC 201
A Survey on Continuous Time Computations
We provide an overview of theories of continuous time computation. These
theories allow us to understand both the hardness of questions related to
continuous time dynamical systems and the computational power of continuous
time analog models. We survey the existing models, summarizing results, and
point to relevant references in the literature
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