Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Option Valuation With Generalized Ant Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Solving Approximation Problems By Ant Colony Programming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Ant Colony Programming for Approximation Problems
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Swarm: The generation of programs by social programming
Natural Computing: an international journal
Eliminating Introns in Ant Colony Programming
Fundamenta Informaticae
Ant colony programming for approximation problems
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
Mining association rules with single and multi-objective grammar guided ant programming
Integrated Computer-Aided Engineering
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This paper begins by reviewing different methods of automatic programming while emphasizing the technique of Ant Programming (AP). AP uses an ant foraging metaphor in which ants generate a program by moving through a graph. Generalized Ant Programming (GAP) uses a context-free grammar and an Ant Colony System (ACS) to guide the program generation search process. There are two enhancements to GAP that are proposed in this paper. These are: providing a heuristic for path termination inspired by building construction and a novel pheromone placement algorithm. Three well-known problems -- Quartic symbolic regression, multiplexer, and an ant trail problem -- are experimentally compared using enhanced GAP (EGAP) and GAP. The results of the experiments show the statistically significant advantage of using this heuristic function and pheromone placement algorithm of EGAP over GAP.