84 citations · 227 across the 25 of their papers we have counts for
26 papers
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…
Genetic Programming Theory and Practice: A Fifteen-Year Trajectory
Moshe Sipper, Jason H. Moore
The GPTP workshop series, which began in 2003, has served over the years as a focal meeting for genetic programming (GP) researchers. As such, we think it provides an excellent sou…
Coevolving Artistic Images Using OMNIREP
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
We have recently developed OMNIREP, a coevolutionary algorithm to discover both a representation and an interpreter that solve a particular problem of interest. Herein, we demonstr…
New Pathways in Coevolutionary Computation
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
The simultaneous evolution of two or more species with coupled fitness -- coevolution -- has been put to good use in the field of evolutionary computation. Herein, we present two n…
Task and Explanation Network
Moshe Sipper
Explainability in deep networks has gained increased importance in recent years. We argue herein that an AI must be tasked not just with a task but also with an explanation of why…
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…
I See Dead People: Gray-Box Adversarial Attack on Image-To-Text Models
Raz Lapid, Moshe Sipper
Modern image-to-text systems typically adopt the encoder-decoder framework, which comprises two main components: an image encoder, responsible for extracting image features, and a…
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…
Classy Ensemble: A Novel Ensemble Algorithm for Classification
Moshe Sipper
We present Classy Ensemble, a novel ensemble-generation algorithm for classification tasks, which aggregates models through a weighted combination of per-class accuracy. Tested ove…
Foiling Explanations in Deep Neural Networks
Snir Vitrack Tamam, Raz Lapid, Moshe Sipper
Deep neural networks (DNNs) have greatly impacted numerous fields over the past decade. Yet despite exhibiting superb performance over many problems, their black-box nature still p…
An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks
Raz Lapid, Zvika Haramaty, Moshe Sipper
Deep neural networks (DNNs) are sensitive to adversarial data in a variety of scenarios, including the black-box scenario, where the attacker is only allowed to query the trained m…
Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution
Eyal Segal, Moshe Sipper
Evolutionary Computation (EC) has been shown to be able to quickly train Deep Artificial Neural Networks (DNNs) to solve Reinforcement Learning (RL) problems. While a Genetic Algor…
High Per Parameter: A Large-Scale Study of Hyperparameter Tuning for Machine Learning Algorithms
Moshe Sipper
Hyperparameters in machine learning (ML) have received a fair amount of attention, and hyperparameter tuning has come to be regarded as an important step in the ML pipeline. But ju…
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…
Combining Deep Learning with Good Old-Fashioned Machine Learning
Moshe Sipper
We present a comprehensive, stacking-based framework for combining deep learning with good old-fashioned machine learning, called Deep GOld. Our framework involves ensemble selecti…
Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE)
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
When seeking a predictive model in biomedical data, one often has more than a single objective in mind, e.g., attaining both high accuracy and low complexity (to promote interpreta…
Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
We have recently presented SAFE -- Solution And Fitness Evolution -- a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate…
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions
Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz
We recently highlighted a fundamental problem recognized to confound algorithmic optimization, namely, \textit{conflating} the objective with the objective function. Even when the…
Binary and Multinomial Classification through Evolutionary Symbolic Regression
Moshe Sipper
We present three evolutionary symbolic regression-based classification algorithms for binary and multinomial datasets: GPLearnClf, CartesianClf, and ClaSyCo. Tested over 162 datase…
Neural Networks with A La Carte Selection of Activation Functions
Moshe Sipper
Activation functions (AFs), which are pivotal to the success (or failure) of a neural network, have received increased attention in recent years, with researchers seeking to design…
Evolution of Activation Functions for Deep Learning-Based Image Classification
Raz Lapid, Moshe Sipper
Activation functions (AFs) play a pivotal role in the performance of neural networks. The Rectified Linear Unit (ReLU) is currently the most commonly used AF. Several replacements…
Symbolic-Regression Boosting
Moshe Sipper, Jason H Moore
Modifying standard gradient boosting by replacing the embedded weak learner in favor of a strong(er) one, we present SyRBo: Symbolic-Regression Boosting. Experiments over 98 regres…
Gamorithm
Moshe Sipper, Jason H. Moore
Examining games from a fresh perspective we present the idea of game-inspired and game-based algorithms, dubbed "gamorithms".
EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover
Patryk Orzechowski, Moshe Sipper, Xiuzhen Huang +1
In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced. The method called EBIC aims to detect biologically meaningful, order-preserving pa…