Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Contextual Genetic Algorithms: Evolving Developmental Rules
Proceedings of the Third European Conference on Advances in Artificial Life
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A study of mate selection in genetic algorithms
A study of mate selection in genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
A game-theoretic memory mechanism for coevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
UMDAs for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A self-organized criticality mutation operator for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Genotype editing and the evolution of regulation and memory
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Information Sciences: an International Journal
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Evolutionary algorithms rarely deal with ontogenetic, non-inherited alteration of genetic information because they are based on a direct genotype-phenotype mapping. In contrast, several processes have been discovered in nature which alter genetic information encoded in DNA before it is translated into amino-acid chains. Ontogenetically altered genetic information is not inherited but extensively used in regulation and development of phenotypes, giving organisms the ability to, in a sense, re-program their genotypes according to environmental cues. An example of post-transcriptional alteration of gene-encoding sequences is the process of RNA Editing. Here we introduce a novel Agent-based model of genotype editing and a computational study of its evolutionary performance in static and dynamic environments. This model builds on our previous Genetic Algorithm with Editing, but presents a fundamentally novel architecture in which coding and non-coding genetic components are allowed to co-evolve. Our goals are: (1) to study the role of RNA Editing regulation in the evolutionary process, (2) to understand how genotype editing leads to a different, and novel evolutionary search algorithm, and (3) the conditions under which genotype editing improves the optimization performance of traditional evolutionary algorithms. We show that genotype editing allows evolving agents to perform better in several classes of fitness functions, both in static and dynamic environments. We also present evidence that the indirect genotype/phenotype mapping resulting from genotype editing leads to a better exploration/exploitation compromise of the search process. Therefore, we show that our biologically-inspired model of genotype editing can be used to both facilitate understanding of the evolutionary role of RNA regulation based on genotype editing in biology, and advance the current state of research in Evolutionary Computation.