Automated Design of Probability Distributions as Mutation Operators for Evolutionary Programming Using Genetic Programming

2013 ◽  
pp. 85-96 ◽  
Author(s):  
Libin Hong ◽  
John Woodward ◽  
Jingpeng Li ◽  
Ender Özcan
Keyword(s):  
Genetic Programming ◽  
Probability Distributions ◽  
Evolutionary Programming ◽  
Automated Design ◽  
Mutation Operators

scholarly journals Automated design of a lightweight block cipher with Genetic Programming

2008 ◽  
Vol 12 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Javier Polimón ◽  
Julio C. Hernández-Castro ◽  
Juan M. Estévez-Tapiador ◽  
Arturo Ribagorda
Keyword(s):  
Genetic Programming ◽  
Block Cipher ◽  
Automated Design ◽  
Lightweight Block Cipher

scholarly journals EVOLUTIONARY ALORITHMS AT CHOICE: FROM GA TO GP

2015 ◽  
Vol 2 ◽  
pp. 185 ◽  
Author(s):  
Peter Grabusts
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Evolutionary Programming ◽  
Stochastic Search ◽  
Evolution Strategies ◽  
Natural Evolution ◽  
Search Methods ◽  
The World ◽  
Classification Tasks

Nowadays the possibilities of evolutionary algorithms are widely used in many optimization and classification tasks. Evolutionary algorithms are stochastic search methods that try to emulate Darwin’s principle of natural evolution. There are (at least) four paradigms in the world of evolutionary algorithms: evolutionary programming, evolution strategies, genetic algorithms and genetic programming. This paper analyzes present-day approaches of genetic algorithms and genetic programming and examines the possibilities of genetic programming that will be used in further research. The paper presents implementation examples that show the working principles of evolutionary algorithms.

scholarly journals Surrogate-assisted Genetic Programming with Simplified Models for Automated Design of Dispatching Rules

2020 ◽  
Author(s):  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Production Systems ◽  
Automated Design ◽  
Research Topic ◽  
Dispatching Rules ◽  
Design System ◽  
Computational Costs ◽  
Powerful Approach ◽  
Effectiveness And Efficiency

© 2013 IEEE. Automated design of dispatching rules for production systems has been an interesting research topic over the last several years. Machine learning, especially genetic programming (GP), has been a powerful approach to dealing with this design problem. However, intensive computational requirements, accuracy and interpretability are still its limitations. This paper aims at developing a new surrogate assisted GP to help improving the quality of the evolved rules without significant computational costs. The experiments have verified the effectiveness and efficiency of the proposed algorithms as compared to those in the literature. Furthermore, new simplification and visualisation approaches have also been developed to improve the interpretability of the evolved rules. These approaches have shown great potentials and proved to be a critical part of the automated design system.

Toward a unified and automated design methodology for multi-domain dynamic systems using bond graphs and genetic programming

Mechatronics ◽  
2003 ◽  
Vol 13 (8-9) ◽  
pp. 851-885 ◽  
Author(s):  
Kisung Seo ◽  
Zhun Fan ◽  
Jianjun Hu ◽  
Erik D. Goodman ◽  
Ronald C. Rosenberg
Keyword(s):  
Genetic Programming ◽  
Dynamic Systems ◽  
Design Methodology ◽  
Automated Design ◽  
Bond Graphs

Automated design error debug using high-level decision diagrams and mutation operators

2013 ◽  
Vol 37 (4-5) ◽  
pp. 505-513 ◽  
Author(s):  
Jaan Raik ◽  
Urmas Repinski ◽  
Anton Chepurov ◽  
Hanno Hantson ◽  
Raimund Ubar ◽  
...  
Keyword(s):  
Automated Design ◽  
Decision Diagrams ◽  
Mutation Operators ◽  
Design Error ◽  
High Level
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