Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
How neutral networks influence evolvability
Complexity
Evolving neural networks through augmenting topologies
Evolutionary Computation
A Taxonomy for artificial embryogeny
Artificial Life
Evolving modular genetic regulatory networks
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Selecting for evolvable representations
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
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
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
Evolvability and speed of evolutionary algorithms in light of recent developments in biology
Journal of Artificial Evolution and Applications
A study of the neutrality of Boolean function landscapes in genetic programming
Theoretical Computer Science
Benchmarking pareto archiving heuristics in the presence of concept drift: diversity versus age
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Genetic representations that do not employ a one-to-one mapping of genotype to phenotype are known as indirect encodings, and can be much more efficient than direct encodings for complex problems. Increasing a representation's capacity to facilitate effective search, i.e. its evolvability, has long been a goal of Evolutionary Computation. However, currently no benchmarks exist to measure evolvability. One reason is that it is difficult to decouple a representation's capacity to evolve under any fitness function, i.e. the latent evolvability, and its performance on a specific benchmark. Towards this goal, a method is proposed in this paper that measures the representation's ability to extract invariant properties from a changing fitness function. The test is applied to three distinct representations and it is able to distinguish all three. Ultimately, this test can serve as the foundation for performing controlled experiments determining what factors contribute to evolvability.