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 and emergent intelligence
Advances in genetic programming
A Representation Scheme To Perform Program Induction in a Canonical Genetic Algorithm
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Empirical studies of the genetic algorithm with noncoding segments
Evolutionary Computation
How neutral networks influence evolvability
Complexity
Evolutive Introns: A Non-Costly Method of Using Introns in GP
Genetic Programming and Evolvable Machines
Introducing Start Expression Genes to the Linkage Learning Genetic Algorithm
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Convergence Time for the Linkage Learning Genetic Algorithm
Evolutionary Computation
Eliminating Introns in Ant Colony Programming
Fundamenta Informaticae
An improved representation for evolving programs
Genetic Programming and Evolvable Machines
Tightness time for the linkage learning genetic algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Reducing population size while maintaining diversity
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Multi Niche parallel GP with a junk-code migration model
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Eliminating Introns in Ant Colony Programming
Fundamenta Informaticae
| Hi-index | 0.00 |
EPI (Evolutionary Program Induction) is an encoding scheme that allows a Genetic Algorithm with a linear, fixed size chromosome to perform the same tasks as does Genetic Programming. Our encoding scheme achieves this by using non-expressed genetic code, i.e. introns. The addition of introns, defined to more closely resemble actual biological usage than is usually done in the GP community, has the unexpected benefit of dynamically reducing the search space. This paper is our initial attempt to prove that this property holds.