Evidence sets and contextual genetic algorithms: exploring uncertainty, context, and embodiment in cognitive and biological systems
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
A study of mate selection in genetic algorithms
A study of mate selection in genetic algorithms
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
Agent-Based Model of Genotype Editing
Evolutionary Computation
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Typical applications of evolutionary optimization in static environments involve the approximation of the extrema of functions. For dynamic environments, the interest is not to locate the extrema but to follow it as closely as possible. This paper compares the extrema-tracking performance of a traditional Genetic Algorithm and a coevolutionary agent-based model of Genotype Editing (ABMGE). This model is constructed using several genetic editing characteristics that are gleaned from the RNA editing system as observed in several organisms. The incorporation of editing mechanisms provides a means for artificial agents with genetic descriptions to gain greater phenotypic plasticity. By allowing the family of editors and the genotypes of agents to co-evolve using the re-generation of editors as a control switch for environmental changes, the artificial agents in ABMGE can discover proper editors to facilitate the tracking of the extrema in dynamic environments. We will show that this agent-based model, together with a coevolutionary mechanism, is more adaptive and robust than the GA. We expect the framework proposed in this paper to advance the current state of research of Evolutionary Computation in dynamic environments.