Approximation model building for design optimization using genetic programming methodology

1998 ◽  
Author(s):  
Vassili Toropov ◽  
Luis Alvarez
Keyword(s):  
Genetic Programming ◽  
Design Optimization ◽  
Model Building ◽  
Approximation Model ◽  
Programming Methodology

Approximation model using genetic programming methodology - Applications

2001 ◽  
Author(s):  
Vassili Toropov ◽  
Luis Alvarez ◽  
David Hughes ◽  
Ashraf Ashour
Keyword(s):  
Genetic Programming ◽  
Approximation Model ◽  
Programming Methodology

scholarly journals On the Evolvability of a Hybrid Ant Colony-Cartesian Genetic Programming Methodology

2021 ◽  
Author(s):  
Sweeney Luis
Keyword(s):  
Genetic Programming ◽  
Dynamic Environment ◽  
Computer Programs ◽  
Hybrid Architecture ◽  
Ant System ◽  
Cartesian Genetic Programming ◽  
Ant Colonies ◽  
Programming Methodology ◽  
Genotype Diversity ◽  
Evolution Proceeds

In this thesis, we design a method that uses Ant Colonies as a Model-based Search to Cartesian Genetic Programming (CGP) to induce computer programs. Candidate problem solutions are encoded using a CGP representation. Ants generate problem solutions guided by pheromone traces of entities and nodes of the CGP representation. The pheromone values are updated based on the paths followed by the best ants, as suggested in the Rank-Based Ant System (ASrank). To assess the evolvability of the system we applied a modified version of the method introduced in [1] to measure rate of evolution which considers variability and neutrality as the major influences in the evolution of a system. Our results show that such method effectively reveals how evolution proceeds under different parameter settings and different environmental scenarios. The proposed hybrid architecture shows high evolvability in a dynamic environment by maintaining a pheromone model that elicits high genotype diversity.

scholarly journals On the Evolvability of a Hybrid Ant Colony-Cartesian Genetic Programming Methodology

2021 ◽  
Author(s):  
Sweeney Luis
Keyword(s):  
Genetic Programming ◽  
Dynamic Environment ◽  
Computer Programs ◽  
Hybrid Architecture ◽  
Ant System ◽  
Cartesian Genetic Programming ◽  
Ant Colonies ◽  
Programming Methodology ◽  
Genotype Diversity ◽  
Evolution Proceeds

In this thesis, we design a method that uses Ant Colonies as a Model-based Search to Cartesian Genetic Programming (CGP) to induce computer programs. Candidate problem solutions are encoded using a CGP representation. Ants generate problem solutions guided by pheromone traces of entities and nodes of the CGP representation. The pheromone values are updated based on the paths followed by the best ants, as suggested in the Rank-Based Ant System (ASrank). To assess the evolvability of the system we applied a modified version of the method introduced in [1] to measure rate of evolution which considers variability and neutrality as the major influences in the evolution of a system. Our results show that such method effectively reveals how evolution proceeds under different parameter settings and different environmental scenarios. The proposed hybrid architecture shows high evolvability in a dynamic environment by maintaining a pheromone model that elicits high genotype diversity.

Variable-Fidelity Multidisciplinary Design Optimization Based on Analytical Target Cascading Framework

2012 ◽  
Vol 544 ◽  
pp. 49-54 ◽  
Author(s):  
Jun Zheng ◽  
Hao Bo Qiu ◽  
Xiao Lin Zhang
Keyword(s):  
Multidisciplinary Design Optimization ◽  
Model Building ◽  
Computational Cost ◽  
Simulation Models ◽  
Multidisciplinary Design ◽  
High Fidelity ◽  
Analytical Target Cascading ◽  
Approximation Model ◽  
Target Cascading ◽  
Variable Fidelity

ATC provides a systematic approach in solving decomposed large scale systems that has solvable subsystems. However， complex engineering system usually has a high computational cost , which result in limiting real-life applications of ATC based on high-fidelity simulation models. To address these problems, this paper aims to develop an efficient approximation model building techniques under the analytical target cascading (ATC) framework, to reduce computational cost associated with multidisciplinary design optimization problems based on high-fidelity simulations. An approximation model building techniques is proposed: approximations in the subsystem level are based on variable-fidelity modeling (interaction of low- and high-fidelity models). The variable-fidelity modeling consists of computationally efficient simplified models (low-fidelity) and expensive detailed (high-fidelity) models. The effectiveness of the method for modeling under the ATC framework using variable-fidelity models is studied. Overall results show the methods introduced in this paper provide an effective way of improving computational efficiency of the ATC method based on variable-fidelity simulation models.

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