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.

Sign in / Sign up

Export Citation Format

Share Document