Notice of Violation of IEEE Publication Principles: Biologically Inspired Evolutionary Agent Systems in Dynamic Environments

Notice of Violation of IEEE Publication Principles

"Biologically Inspired Evolutionary Agent Systems in Dynamic Environments"
by K.W. Yeom and J.H. Park
in the Proceedings of the IEEE Congress on Evolutionary Computation, 2006. CEC 2006, pp.386-390

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"Evolving Complete Agents Using Artificial Ontogeny"
by J.C. Bongard and R. Pfeifer,
in Morpho-functional Machines: The New Species (Designing Embodied Intelligence), Springer-Verlag, pp. 237-258


We introduce a new bio-inspired agent system based on vertebrate immune system and describe the evolutionary metaphor for adaptation to dynamic environments using genetic algorithm. Biological information processing systems have various interesting characteristics from an engineering viewpoint. Among them, the immune system plays an important role in maintaining its own system against dynamically changing environments. Based on this fact, we have investigated a new decentralized consensus-making system for the behavior of autonomous mobile robots, inspired by the immune idiotypic network hypothesis in immunology. The developmental encoding scheme is used to translate a given genotype into a complete agent, which then acts in a physically-realistic virtual environment. Evolution is accomplished using a genetic algorithm, in which the genotypes are treated as genetic regulatory networks. The dynamics of the regulatory network direct the growth of the agent, and lead to the construction of both the morphology and neural control of the agent. We demonstrate that such a model can be used to evolve agents to perform non-trivial tasks, such as directed locomotion and block pushing in a noisy environment.