Abstract
This paper investigates how grammatical genetic programming performs for evolving simple integer list manipulation functions. We propose three sub-problems which are related to, or component of integer sorting problem as defined by genetic programming literature. We further investigate the effects of modifying evolutionary parameters, such as the number of generations allowed, number of populations, and number of test cases, on the number and distribution of successful solutions. Finally, we propose an AST based dead-code removal for the intron induced non-functional codes on evolved individuals.
Keywords
Computer science
Genetic programming
Integer programming
Test (biology)
Grammatical evolution
Integer (computer science)
Population
Programming language
Genetic algorithm
Artificial intelligence
Algorithm
Machine learning
Demography
Biology
Subject Areas
Evolutionary Algorithms and Applications
·Artificial Intelligence ·Physical Sciences
Reinforcement Learning in Robotics
·Artificial Intelligence ·Physical Sciences
Metaheuristic Optimization Algorithms Research
·Artificial Intelligence ·Physical Sciences
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