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…
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…
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…
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…