Fitness Approximation through Machine Learning

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.

A Melting Pot of Evolution and Learning

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.

Classification Evolutionary Algorithms +3

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

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.

BIG-bench Machine Learning