From d42f3f546ea8b79b3d4bbf57e0ca2ad75a9ef6bd Mon Sep 17 00:00:00 2001
From: Sebastian <sebastian.goetz@acm.org>
Date: Thu, 20 Jun 2024 11:43:29 +0000
Subject: [PATCH] Commit by Android-App
---
deletedItems.bib | 18 ++++++++++++++++++
temp.bib | 18 ------------------
2 files changed, 18 insertions(+), 18 deletions(-)
diff --git a/deletedItems.bib b/deletedItems.bib
index 3eb4cff..2da583f 100644
--- a/deletedItems.bib
+++ b/deletedItems.bib
@@ -212,4 +212,22 @@
keywords = {gradual programming language, hedy, the technology acceptance model},
location = {Dublin, Ireland},
series = {ITiCSE '22}
+}
+
+@inproceedings{10.1145/2576768.2598288,
+ author = {Krawiec, Krzysztof and O'Reilly, Una-May},
+ title = {Behavioral programming: a broader and more detailed take on semantic GP},
+ year = {2014},
+ isbn = {9781450326629},
+ publisher = {Association for Computing Machinery},
+ address = {New York, NY, USA},
+ url = {https://doi.org/10.1145/2576768.2598288},
+ doi = {10.1145/2576768.2598288},
+ abstract = {In evolutionary computation, the fitness of a candidate solution conveys sparse feedback. Yet in many cases, candidate solutions can potentially yield more information. In genetic programming (GP), one can easily examine program behavior on particular fitness cases or at intermediate execution states. However, how to exploit it to effectively guide the search remains unclear. In this study we apply machine learning algorithms to features describing the intermediate behavior of the executed program. We then drive the standard evolutionary search with additional objectives reflecting this intermediate behavior. The machine learning functions independent of task-specific knowledge and discovers potentially useful components of solutions (subprograms), which we preserve in an archive and use as building blocks when composing new candidate solutions. In an experimental assessment on a suite of benchmarks, the proposed approach proves more capable of finding optimal and/or well-performing solutions than control methods.},
+ booktitle = {Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation},
+ pages = {935–942},
+ numpages = {8},
+ keywords = {archive, behavioral evaluation, genetic programming, multiobjective evolutionary computation, program semantics, program synthesis, search operators},
+ location = {Vancouver, BC, Canada},
+ series = {GECCO '14}
}
\ No newline at end of file
diff --git a/temp.bib b/temp.bib
index 416f145..b26b0d6 100644
--- a/temp.bib
+++ b/temp.bib
@@ -67,24 +67,6 @@
series = {ECOOP'10}
}
-@inproceedings{10.1145/2576768.2598288,
- author = {Krawiec, Krzysztof and O'Reilly, Una-May},
- title = {Behavioral programming: a broader and more detailed take on semantic GP},
- year = {2014},
- isbn = {9781450326629},
- publisher = {Association for Computing Machinery},
- address = {New York, NY, USA},
- url = {https://doi.org/10.1145/2576768.2598288},
- doi = {10.1145/2576768.2598288},
- abstract = {In evolutionary computation, the fitness of a candidate solution conveys sparse feedback. Yet in many cases, candidate solutions can potentially yield more information. In genetic programming (GP), one can easily examine program behavior on particular fitness cases or at intermediate execution states. However, how to exploit it to effectively guide the search remains unclear. In this study we apply machine learning algorithms to features describing the intermediate behavior of the executed program. We then drive the standard evolutionary search with additional objectives reflecting this intermediate behavior. The machine learning functions independent of task-specific knowledge and discovers potentially useful components of solutions (subprograms), which we preserve in an archive and use as building blocks when composing new candidate solutions. In an experimental assessment on a suite of benchmarks, the proposed approach proves more capable of finding optimal and/or well-performing solutions than control methods.},
- booktitle = {Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation},
- pages = {935–942},
- numpages = {8},
- keywords = {archive, behavioral evaluation, genetic programming, multiobjective evolutionary computation, program semantics, program synthesis, search operators},
- location = {Vancouver, BC, Canada},
- series = {GECCO '14}
-}
-
@inproceedings{10.1145/2414639.2414650,
author = {Ricci, Alessandro and Santi, Andrea},
title = {Programming abstractions for integrating autonomous and reactive behaviors: an agent-oriented approach},
--
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