NP-Completeness of deciding binary genetic encodability

Authors:
Andreas Blass;Boris Mitavskiy
Affiliations:
Department of Mathematics, University of Michigan, Ann Arbor, MI, United States;School of Computer Science, University of Birmingham, Birmingham, Great Britain
Venue:
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Year:
2005

Citing 10
Cited 0

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
The Simple Genetic Algorithm: Foundations and Theory

The Simple Genetic Algorithm: Foundations and Theory
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Network random keys: a tree representation scheme for genetic and evolutionary algorithms

Evolutionary Computation
Group properties of crossover and mutation

Evolutionary Computation
Hyperschema Theory for GP with One-Point Crossover, Building Blocks, and Some New Results in GA Theory

Proceedings of the European Conference on Genetic Programming
Form Invariance and Implicit Parallelism

Evolutionary Computation
Crossover Invariant Subsets of the Search Space for Evolutionary Algorithms

Evolutionary Computation
Applying adaptive algorithms to epistatic domains

IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Comparing evolutionary computation techniques via their representation

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI

Quantified Score

Hi-index	0.00

Visualization

Abstract

In previous work of the second author a rigorous mathematical foundation for re-encoding one evolutionary search algorithm by another has been developed. A natural issue to consider then is the complexity of deciding whether or not a given evolutionary algorithm can be re-encoded by one of the standard classical evolutionary algorithms such as a binary genetic algorithm. In the current paper we prove that, in general, this decision problem is NP-complete.