Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming!
Implementing Asynchronous Embryonic Circuits using AARDVArc
EH '02 Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware (EH'02)
Computational embryology: past, present and future
Advances in evolutionary computing
Genetic Programming and Evolvable Machines
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
PLAZZMID: an evolutionary agent-based architecture inspired by Bacteria and Bees
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Fractal gene regulatory networks for robust locomotion control of modular robots
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Fractal gene regulatory networks for control of nonlinear systems
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
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Fractal proteins are an evolvable method of mapping genotype to phenotype through a developmental process, where genes are expressed into proteins comprised of subsets of the Mandelbrot Set. The resulting network of gene and protein interactions can be designed by evolution to produce specific patterns that in turn can be used to solve problems. In this paper, adaptive developmental programs, capable of developing different solutions in response to different signals from an environment, are investigated. The evolvability of solutions and the capability of these solutions to survive damage is assessed. Evolution is used to create a fractal gene regulatory network (GRN) thatcalculates the squareroot of the input (its environment). This is compared with a GP-evolved squareroot function and a human-designed squareroot function. The programs are damaged by corrupting their compiled executable code, and the ability for each of them to survive such damage is assessed. Experiments demonstrate that only the evolutionary developmental code shows gracefuldegradation after damage. This provides evidence that software based on gene, protein and cellular computation is far more robust than traditional methods. Like a multicellular organism, with its genes evolved and developed, it shows graceful degradation. Should it be damaged, it is designed to continue to work.