Search Results for author: Achiya Elyasaf

Found 6 papers, 2 papers with code

Deep Neural Crossover

no code implementations17 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.

Fitness Approximation through Machine Learning

1 code implementation6 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.

A Melting Pot of Evolution and Learning

no code implementations8 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.

Classification Evolutionary Algorithms +3

EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration

2 code implementations21 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.

BIG-bench Machine Learning

Scenario-Assisted Deep Reinforcement Learning

no code implementations9 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.

reinforcement-learning Reinforcement Learning (RL)

Evolving Context-Aware Recommender Systems With Users in Mind

no code implementations30 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.

feature selection Recommendation Systems

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