- author = "Ruchira Sasanka and Konstantinos Krommydas",
- title = "An Evolutionary Framework for Automatic and Guided Discovery of Algorithms",
- howpublished = "arXiv",
- year = "2019",
- month = "5 " # apr,
- keywords = "genetic algorithms, genetic programming, AAD, PGE, evolutionary strategies, Synthesis, Evolution, Python, HPC",
- eprint = "1904.02830",
-
URL = "
http://arxiv.org/abs/1904.02830",
-
biburl = "https://dblp.org/rec/journals/corr/abs-1904-02830.bib",
- bibsource = "dblp computer science bibliography, https://dblp.org",
- timestamp = "Wed, 24 Apr 2019 12:21:25 +0200",
- size = "13 pages",
-
abstract = "Automatic Algorithm Discoverer (AAD), an evolutionary
framework for synthesizing programs of high complexity.
To guide evolution, prior evolutionary algorithms have
depended on fitness (objective) functions, which are
challenging to design. To make evolutionary progress,
instead, AAD employs Problem Guided Evolution (PGE),
which requires introduction of a group of problems
together. With PGE, solutions discovered for simpler
problems are used to solve more complex problems in the
same group. PGE also enables several new evolutionary
strategies, and naturally yields to High-Performance
Computing (HPC) techniques.
PGE and related evolutionary strategies enable AAD to discover algorithms of similar or higher complexity relative to the state-of-the-art. Specifically, AAD produces Python code for 29 array/vector problems ranging from min, max, reverse, to more challenging problems like sorting and matrix-vector multiplication. Additionally, we find that AAD shows adaptability to constrained environments/inputs and demonstrates outside-of-the-box problem solving abilities.",
-
notes = "Please contact authors for source code (main.py)
See also \cite{Sasanka:2020:CF}
Intel",
