26 citations · 80 across the 14 of their papers we have counts for
6 papers · 1 filter
Deep Learning-Based Operators for Evolutionary Algorithms
Eliad Shem-Tov, Moshe Sipper, Achiya Elyasaf
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 ge…
Evolving Assembly Code in an Adversarial Environment
Irina Maliukov, Gera Weiss, Oded Margalit +1
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 att…
Deep Neural Crossover
Eliad Shem-Tov, Achiya Elyasaf
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 ran…
Fitness Approximation through Machine Learning
Itai Tzruia, Tomer Halperin, Moshe Sipper +1
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…
A Melting Pot of Evolution and Learning
Moshe Sipper, Achiya Elyasaf, Tomer Halperin +5
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 Classific…
EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration
Moshe Sipper, Tomer Halperin, Itai Tzruia +1
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 s…