Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Elyasaf, Achiya"'
We present two novel domain-independent genetic operators that harness the capabilities of deep learning: a crossover operator for genetic algorithms and a mutation operator for genetic programming. Deep Neural Crossover leverages the capabilities of
Externí odkaz:
http://arxiv.org/abs/2407.10477
One of the benefits of using executable specifications such as Behavioral Programming (BP) is the ability to align the system implementation with its requirements. This is facilitated in BP by a protocol that allows independent implementation modules
Externí odkaz:
http://arxiv.org/abs/2404.01858
In this work, we evolve Assembly code for the CodeGuru competition. The goal is to create a survivor -- an Assembly program that runs the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolvi
Externí odkaz:
http://arxiv.org/abs/2403.19489
Autor:
Shem-Tov, Eliad, Elyasaf, Achiya
We present a novel multi-parent crossover operator in genetic algorithms (GAs) called ``Deep Neural Crossover'' (DNC). Unlike conventional GA crossover operators that rely on a random selection of parental genes, DNC leverages the capabilities of dee
Externí odkaz:
http://arxiv.org/abs/2403.11159
We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine-learning (ML) models, through dynamic adaptation to the evolutionary state. Maintaining a dataset of sampled individuals along with their actual
Externí odkaz:
http://arxiv.org/abs/2309.03318
We present Provengo, a comprehensive suite of tools designed to facilitate the implementation of Scenario-Driven Model-Based Testing (SDMBT), an innovative approach that utilizes scenarios to construct a model encompassing the user's perspective and
Externí odkaz:
http://arxiv.org/abs/2308.15938
Autor:
Sipper, Moshe, Elyasaf, Achiya, Halperin, Tomer, Haramaty, Zvika, Lapid, Raz, Segal, Eyal, Tzruia, Itai, Tamam, Snir Vitrack
We survey eight recent works by our group, involving the successful blending of evolutionary algorithms with machine learning and deep learning: 1. Binary and Multinomial Classification through Evolutionary Symbolic Regression, 2. Classy Ensemble: A
Externí odkaz:
http://arxiv.org/abs/2306.04971
Publikováno v:
SoftwareX, 22, 2023, 101381, ISSN 2352-7110
EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under the BSD 3-Clause License, and compatible with scikit-learn. Designed with modern software engineering and machine learning integration in mind, EC-KitY
Externí odkaz:
http://arxiv.org/abs/2207.10367
Publikováno v:
in IEEE Transactions on Software Engineering, vol. 49, no. 4, pp. 2303-2317, 1 April 2023
We identify and demonstrate a weakness of Petri Nets (PN) in specifying composite behavior of reactive systems. Specifically, we show how, when specifying multiple requirements in one PN model, modelers are obliged to specify mechanisms for combining
Externí odkaz:
http://arxiv.org/abs/2205.00221
Publikováno v:
In Proceedings of MODELSWARD 2022: the 10th International Conference on Model-Driven Engineering and Software Development, ISBN 978-989-758-550-0, ISSN 2184-4348, pages 310-319
Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this wo
Externí odkaz:
http://arxiv.org/abs/2202.04337