Event-based graphical monitoring in the EpochX genetic programming framework

Vaseux, Loïc; Otero, Fernando E. B.; Castle, Tom; Johnson, Colin G.

doi:10.1145/2464576.2482710

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…Whereas some previous studies focused on visualising the ancestry of the evolutionary process [1], [2], [4], [5], [7], ECvis instead focuses on visualising the fitness and distribution of each dimension of the population at any iteration. However, in contrast to [3], ECvis does not use any dimensionality reduction techniques.…”

Section: Evolutionary Computation Visualisation (Ecvis)mentioning

confidence: 99%

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Evolutionary Computation Visualization: ECvis

2023

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Section: Evolutionary Computation Visualisation (Ecvis)mentioning

confidence: 99%

“…A second visualisation technique used for EAs is to collect ancestry information during execution of the algorithm that provides a visual representation of the parent-child relationships [1], [2], [7], for example as a graph [4], [5] (see Fig. 2).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Computation Visualization: ECvis

2023

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“…Event-based techniques for programming have been using continuations which are delimited monadic [17,18]. Fork-join, task, async, and event functions appear not to rely on a specific language design.…”

Section: Related and Future Workmentioning

confidence: 99%

Asynchronous Programming in a Prioritized Form

El-Zawawy

2015

Preprint

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Asynchronous programming has appeared as a programming style that overcomes undesired properties of concurrent programming. Typically in asynchronous models of programming, methods are posted into a post list for latter execution. The order of method executions is serial, but nondeterministic. This paper presents a new and simple, yet powerful, model for asynchronous programming. The proposed model consists of two components; a context-free grammar and an operational semantics. The model is supported by the ability to express important applications. An advantage of our model over related work is that the model simplifies the way posted methods are assigned priorities. Another advantage is that the operational semantics uses the simple concept of singly linked list to simulate the prioritized process of methods posting and execution. The simplicity and expressiveness make it relatively easy for analysis algorithms to disclose the otherwise un-captured programming bugs in asynchronous programs.

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“…One way to move past the limitations of summary statistics is to collect ancestry information on runs, recording and analyzing parent-child relationships [3,4,12]. Most previous ancestry work Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Genetic Programming Ancestries

McPhee

Casale

Finzel

et al. 2016

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

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Previous work has demonstrated the utility of graph databases as a tool for collecting, analyzing, and visualizing ancestry in evolutionary computation runs. That work focused on sections of individual runs, whereas this paper illustrates the application of these ideas on the entirety of large runs (up to three hundred thousand individuals) and combinations of multiple runs. Here we use these tools to generate graphs showing all the ancestors of successful individuals from a variety of stack-based genetic programming runs on software synthesis problems. These graphs highlight important moments in the evolutionary process. They also allow us to compare the dynamics for successful and unsuccessful runs. As well as displaying these full ancestry graphs, we use a variety of standard techniques such as size, color, pattern, labeling, and opacity to visualize other important information such as fitness, which genetic operators were used, and the distance between parent and child genomes. While this generates an extremely rich visualization, the amount of data can also be somewhat overwhelming, so we also explore techniques for filtering these graphs that allow us to better understand the key dynamics.

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Event-based graphical monitoring in the EpochX genetic programming framework

Cited by 4 publications

References 10 publications

Evolutionary Computation Visualization: ECvis

Evolutionary Computation Visualization: ECvis

Asynchronous Programming in a Prioritized Form

Visualizing Genetic Programming Ancestries

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