33 Result(s)
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Chapter and Conference Paper
Genetic Programming with Synthetic Data for Interpretable Regression Modelling and Limited Data
A trained regression model can be used to create new synthetic training data by drawing from a distribution over independent variables and calling the model to produce a prediction for the dependent variable. ...
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Chapter
Representation Learning for the Arts: A Case Study Using Variational Autoencoders for Drum Loops
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Chapter and Conference Paper
A Multivocal Literature Review of Function-as-a-Service (FaaS) Infrastructures and Implications for Software Developers
In this paper, we provide a multivocal literature review of Function as a Service (FaaS) infrastructures. FaaS is an important, emerging category of cloud computing, which requires that software applications a...
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Chapter
Genetic Programming Symbolic Regression: What Is the Prior on the Prediction?
In the context of Genetic Programming Symbolic Regression, we empirically investigate the prior on the output prediction, that is, the distribution of the output prior to observing data. We distinguish between...
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Chapter and Conference Paper
Program Synthesis in a Continuous Space Using Grammars and Variational Autoencoders
An important but elusive goal of computer scientists is the automatic creation of computer programs given only input and output examples. We present a novel approach to program synthesis based on the combin...
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Chapter and Conference Paper
Program Trace Optimization with Constructive Heuristics for Combinatorial Problems
Program Trace Optimisation (PTO), a highly general optimisation framework, is applied to a range of combinatorial optimisation (COP) problems. It effectively combines “smart” problem-specific constructive heur...
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Chapter and Conference Paper
Why Is Auto-Encoding Difficult for Genetic Programming?
Unsupervised learning is an important component in many recent successes in machine learning. The autoencoder neural network is one of the most prominent approaches to unsupervised learning. Here, we use the g...
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Chapter and Conference Paper
Subject Recognition Using Wrist-Worn Triaxial Accelerometer Data
This study demonstrates how a subject can be identified by the means of accelerometer data generated through wrist-worn devices in the context of clinical trials where data integrity is of utmost importance. A...
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Chapter
Geometric Semantic Grammatical Evolution
Geometric Semantic Genetic Programming (GSGP) is a novel form of Genetic Programming (GP), based on a geometric theory of evolutionary algorithms, which directly searches the semantic space of programs. In thi...
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Chapter and Conference Paper
Program Trace Optimization
We introduce Program Trace Optimization (PTO), a system for ‘universal heuristic optimization made easy’. This is achieved by strictly separating the problem from the search algorithm. New problem definitions ...
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Chapter and Conference Paper
A Hybrid Autoencoder and Density Estimation Model for Anomaly Detection
A novel one-class learning approach is proposed for network anomaly detection based on combining autoencoders and density estimation. An autoencoder attempts to reproduce the input data in the output layer. Th...
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Chapter and Conference Paper
Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Data
Credit card classification based on machine learning has attracted considerable interest from the research community. One of the most important tasks in this area is the ability of classifiers to handle the im...
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Chapter and Conference Paper
Grammatical Music Composition with Dissimilarity Driven Hill Climbing
An algorithmic compositional system that uses hill climbing to create short melodies is presented. A context free grammar maps each section of the resultant individual to a musical segment resulting in a serie...
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Chapter and Conference Paper
One-Class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming
A novel approach is proposed for fast anomaly detection by one-class classification. Standard kernel density estimation is first used to obtain an estimate of the input probability density function, based on t...
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Chapter
Genetic Programming
Genetic programming (GP ) is the subset of evolutionary computation in which the aim is to create executable programs. It is an exciting field with many applications, some immediate and practical, others long-te...
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Chapter and Conference Paper
Geometric Semantic Genetic Programming for Financial Data
We cast financial trading as a symbolic regression problem on the lagged time series, and test a state of the art symbolic regression method on it. The system is geometric semantic genetic programming, which a...
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Chapter and Conference Paper
Higher Order Functions for Kernel Regression
Kernel regression is a well-established nonparametric method, in which the target value of a query point is estimated using a weighted average of the surrounding training examples. The weights are typically ob...
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Chapter and Conference Paper
Measuring Mutation Operators’ Exploration-Exploitation Behaviour and Long-Term Biases
We propose a simple method of directly measuring a mutation operator’s short-term exploration-exploitation behaviour, based on its transition matrix. Higher values for this measure indicate a more exploitative...
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Chapter
FlexGP.py: Prototy** Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud
Running genetic programming on the cloud presents researchers with great opportunities and challenges. We argue that standard island algorithms do not have the properties of elasticity and robustness required ...
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Chapter and Conference Paper
Program Optimisation with Dependency Injection
For many real-world problems, there exist non-deterministic heuristics which generate valid but possibly sub-optimal solutions. The program optimisation with dependency injection method, introduced here, allows s...
