no code implementations • 17 Mar 2024 • Eliad Shem-Tov, Achiya Elyasaf
Unlike conventional GA crossover operators that rely on a random selection of parental genes, DNC leverages the capabilities of deep reinforcement learning (DRL) and an encoder-decoder architecture to select the genes.
1 code implementation • 6 Sep 2023 • Itai Tzruia, Tomer Halperin, Moshe Sipper, Achiya Elyasaf
We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine-learning (ML) models, focusing on evolutionary agents in Gymnasium (game) simulators -- where fitness computation is costly.
no code implementations • 8 Jun 2023 • Moshe Sipper, Achiya Elyasaf, Tomer Halperin, Zvika Haramaty, Raz Lapid, Eyal Segal, Itai Tzruia, Snir Vitrack Tamam
We survey eight recent works by our group, involving the successful blending of evolutionary algorithms with machine learning and deep learning: 1.
2 code implementations • 21 Jul 2022 • Moshe Sipper, Tomer Halperin, Itai Tzruia, Achiya Elyasaf
EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under the BSD 3-Clause License, and compatible with scikit-learn.
no code implementations • 9 Feb 2022 • Raz Yerushalmi, Guy Amir, Achiya Elyasaf, David Harel, Guy Katz, Assaf Marron
In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints.
no code implementations • 30 Jul 2020 • Amit Livne, Eliad Shem Tov, Adir Solomon, Achiya Elyasaf, Bracha Shapira, Lior Rokach
An empirical analysis of our results validates that our proposed approach outperforms SOTA CARS models while improving transparency and explainability to the user.