Multitask linear genetic programming with shared individuals and its application to dynamic job shop scheduling
Multitask genetic programming methods have been applied to various domains, such as
classification, regression, and combinatorial optimization problems. Most existing multitask …
classification, regression, and combinatorial optimization problems. Most existing multitask …
Fitness Landscape Optimization Makes Stochastic Symbolic Search By Genetic Programming Easier
Searching for symbolic models plays an important role in a wide range of domains such as
neural architecture search and automatic program synthesis. Genetic programming is a …
neural architecture search and automatic program synthesis. Genetic programming is a …
Linear matrix genetic programming as a tool for data-driven black-box control-oriented modeling in conditions of limited access to training data
T Praczyk, M Szymkowiak - Scientific Reports, 2024 - nature.com
In the paper, a new evolutionary technique called Linear Matrix Genetic Programming
(LMGP) is proposed. It is a matrix extension of Linear Genetic Programming and its …
(LMGP) is proposed. It is a matrix extension of Linear Genetic Programming and its …
A further investigation to improve linear genetic programming in dynamic job shop scheduling
Dynamic Job Shop Scheduling (DJSS) is an important problem with many real-world
applications. Genetic programming is a promising technique to solve DJSS, which …
applications. Genetic programming is a promising technique to solve DJSS, which …
Bridging directed acyclic graphs to linear representations in linear genetic programming: a case study of dynamic scheduling
Linear genetic programming (LGP) is a genetic programming paradigm based on a linear
sequence of instructions being executed. An LGP individual can be decoded into a directed …
sequence of instructions being executed. An LGP individual can be decoded into a directed …
The pole balancing problem from the viewpoint of system flexibility
LFDP Sotto, S Mayer, J Garcke - Proceedings of the Genetic and …, 2022 - dl.acm.org
Whereas evolutionary computation usually solves problems from scratch, organisms evolve
under changing environments and possess flexibility, adapting from being good at one task …
under changing environments and possess flexibility, adapting from being good at one task …
Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization
LFDP Sotto, S Mayer, H Janarthanam, A Butz… - arXiv preprint arXiv …, 2023 - arxiv.org
Optimizing manufacturing process parameters is typically a multi-objective problem with
often contradictory objectives such as production quality and production time. If production …
often contradictory objectives such as production quality and production time. If production …
Reflecting on Thirty Years of ECJ
K De Jong, E Hart - Evolutionary Computation, 2023 - direct.mit.edu
We reflect on 30 years of the journal Evolutionary Computation. Taking the papers published
in the first volume in 1993 as a springboard, as the founding and current Editors-in-Chief, we …
in the first volume in 1993 as a springboard, as the founding and current Editors-in-Chief, we …
Graph-based genetic programming
Zdenek Vašıcek received all his degrees from Brno University of Technology, Czech
Republic, where he is currently an Associate professor. He holds a Ph. D.(2012) and an MS …
Republic, where he is currently an Associate professor. He holds a Ph. D.(2012) and an MS …
Crossover Destructiveness in Cartesian versus Linear Genetic Programming
M Kocherovsky, W Banzhaf - ALIFE 2024: Proceedings of the 2024 …, 2024 - direct.mit.edu
Abstract Cartesian Genetic Programming (CGP) literature repeatedly reports that crossover
operators hinder CGP search compared to a 1+ λ strategy based on mutation only. Though …
operators hinder CGP search compared to a 1+ λ strategy based on mutation only. Though …