69,392 research outputs found
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care
The huge wealth of data in the health domain can be exploited to create
models that predict development of health states over time. Temporal learning
algorithms are well suited to learn relationships between health states and
make predictions about their future developments. However, these algorithms:
(1) either focus on learning one generic model for all patients, providing
general insights but often with limited predictive performance, or (2) learn
individualized models from which it is hard to derive generic concepts. In this
paper, we present a middle ground, namely parameterized dynamical systems
models that are generated from data using a Genetic Programming (GP) framework.
A fitness function suitable for the health domain is exploited. An evaluation
of the approach in the mental health domain shows that performance of the model
generated by the GP is on par with a dynamical systems model developed based on
domain knowledge, significantly outperforms a generic Long Term Short Term
Memory (LSTM) model and in some cases also outperforms an individualized LSTM
model
Automatic programming methodologies for electronic hardware fault monitoring
This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the Stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.This research was supported by the International Joint Research Grant of the IITA (Institute of Information Technology Assessment) foreign professor invitation program of the MIC (Ministry of Information and Communication), Korea
Neural Network architectures design by Cellular Automata evolution
4th Conference of Systemics Cybernetics and Informatics. Orlando, 23-26 July 2000The design of the architecture is a crucial step in the successful application of a neural network. However, the architecture design is basically, in most cases, a human experts job. The design depends heavily on both, the expert experience and on a tedious trial-and-error process. Therefore, the development of automatic methods to determine the architecture of feedforward neural networks is a field of interest in the neural network community. These methods are generally based on search techniques, as genetic algorithms, simulated annealing or evolutionary strategies. Most of the designed methods are based on direct representation of the parameters of the network. This representation does not allow scalability, so to represent large architectures very large structures are required. In this work, an indirect constructive encoding scheme is proposed to find optimal architectures of feed-forward neural networks. This scheme is based on cellular automata representations in order to increase the scalability of the method.Publicad
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
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