Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to Kolmogorov complexity and its applications
An introduction to Kolmogorov complexity and its applications
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Object-Oriented Software Construction
Object-Oriented Software Construction
A Taxonomy for artificial embryogeny
Artificial Life
Generative representations for evolutionary design automation
Generative representations for evolutionary design automation
Modularity in design of products and systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Analyzing the effects of module encapsulation on search space bias
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Developmental evaluation in Genetic Programming: The TAG-based frame work
International Journal of Knowledge-based and Intelligent Engineering Systems - Genetic Programming An Emerging Engineering Tool
Measures of complexity for artificial embryogeny
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Developmental Gene Regulation Network for Constructing Electronic Circuits
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
On the performance effects of unbiased module encapsulation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolving modular neural-networks through exaptation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Genotype reuse more important than genotype size in evolvability of embodied neural networks
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Investigating whether hyperNEAT produces modular neural networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A novel generative encoding for evolving modular, regular and scalable networks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On the relationships between synaptic plasticity and generative systems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Combining structural analysis and multi-objective criteria for evolutionary architectural design
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Blindbuilder: a new encoding to evolve lego-like structures
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Developmental evaluation in genetic programming: the preliminary results
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
A local search interface for interactive evolutionary architectural design
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Impact of neuron models and network structure on evolving modular robot neural network controllers
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Heterochronic scaling of developmental durations in evolved soft robots
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A methodology for user directed search in evolutionary design
Genetic Programming and Evolvable Machines
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For computer-automated design systems to scale to complex designs they must be able to produce designs that exhibit the characteristics of modularity, regularity and hierarchy -- characteristics that are found both in man-made and natural designs. Here we claim that these characteristics are enabled by implementing the attributes of combination, control-flow and abstraction in the representation.To support this claim we use an evolutionary algorithm to evolve solutions to different sizes of a table design problem using five different representations, each with different combinations of modularity, regularity and hierarchy enabled and show that the best performance happens when all three of these attributes are enabled.We also define metrics for modularity, regularity and hierarchy in design encodings and demonstrate that high fitness values are achieved with high values of modularity, regularity and hierarchy and that there is a positive correlation between increases in fitness and increases in the measured values of modularity, regularity and hierarchy.