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Evolutionary algorithms (EAs) are a kind of stochastic optimization methods, which have been testified to be powerful in solving many real-world hard problems in past decades. But till now, we are still short of effective methods to represent and ...
It is our great pleasure to welcome you to the International Workshop on Evolutionary Rule-Based Machine Learning Workshop held in conjunction with GECCO 2016. In previous years, this workshop has been hosted as the International Workshop on Learning ...
Code Fragments (CFs) have existed as an extension to Evolutionary Computation, specifically Learning Classifiers Systems (LCSs), for half a decade. Through the scaling, abstraction and reuse of both knowledge and functionality that CFs enable, ...
Learning classifier systems (LCSs) are rule-based evolutionary algorithms uniquely suited to classification and data mining in complex, multi-factorial, and heterogeneous problems. LCS rule fitness is commonly based on accuracy, but this metric alone is ...
Introduction and objective: Computer Aided Decision (CAD) systems based on Medical Imaging could support radiologists in classifying malignant regions from benign ones, in the field of investigation for breast cancer detection. This decision may often ...
This paper focuses on the problem of physical activity recognition, i.e., the development of a system which is able to learn patterns from data in order to be able to detect which physical activity (e.g. running, walking, ascending stairs, etc.) a ...
Parkinson's disease (PD) is a chronic neurodegenerative condition. Traditionally categorised as a movement disorder, nowadays it is recognised that PD can also lead to significant cognitive dysfunction including, in many cases, full-blown dementia. Due ...
Evolutionary Algorithms (EAs) are inherently parallel due to their ability to simultaneously evaluate the fitness of individuals. Synchronous Parallel EAs (SPEAs) leverage this with the intent to gain significant speed-ups when executed on multiple ...
Automatically designing algorithms has long been a dream of computer scientists. Early attempts which generate computer programs from scratch, have failed to meet this goal. However, in recent years there have been a number of different technologies ...
In autoconstructive evolutionary algorithms, individuals implement not only candidate solutions to specified computational problems, but also their own methods for variation of offspring. This makes it possible for the variation methods to themselves ...
JavaScript is an interpreted language mainly known for its inclusion in web browsers, making them a container for rich Internet based applications. This has inspired its use, for a long time, as a tool for evolutionary algorithms, mainly so in browser-...
elephant56 is an open source framework for the development and execution of single and parallel Genetic Algorithms (GAs). It provides high level functionalities that can be reused by developers, who no longer need to worry about complex internal ...
Compiler flag selection can be an effective way to increase the quality of executable code according to different code quality criteria. Evolutionary algorithms have been successfully applied to this optimization problem. However, previous approaches ...
Genetic Programming (GP) has been criticized for targeting irrelevant problems [12], and is true of the wider machine learning community [11]. which has become detached from the source of the data it is using to drive the field forward. However, ...
Genetic programming (GP) is an evolutionary-based search paradigm that is well suited to automatically solve difficult design problems. The general principles of GP have been used to evolve mathematical functions, models, image operators, programs, and ...