Introduction to artificial life
Introduction to artificial life
How neutral networks influence evolvability
Complexity
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Evolving neural networks through augmenting topologies
Evolutionary Computation
Emergence of modularity in genotype-phenotype mappings
Artificial Life
A Taxonomy for artificial embryogeny
Artificial Life
Towards an empirical measure of evolvability
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Compact representations as a search strategy: compression EDAs
Theoretical Computer Science - Foundations of genetic algorithms
Acquiring evolvability through adaptive representations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Neutrality and variability: two sides of evolvability in linear genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
Evolvability and speed of evolutionary algorithms in light of recent developments in biology
Journal of Artificial Evolution and Applications
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Evolutionary algorithms tend to produce solutions that are not evolvable: Although current fitness may be high, further search is impeded as the effects of mutation and crossover become increasingly detrimental. In nature, in addition to having high fitness, organisms have evolvable genomes: phenotypic variation resulting from random mutation is structured and robust. Evolvability is important because it allows the population to produce meaningful variation, leading to efficient search. However, because evolvability does not improve immediate fitness, it must be selected for indirectly. One way to establish such a selection pressure is to change the fitness function systematically. Under such conditions, evolvability emerges only if the representation allows manipulating how genotypic variation maps onto phenotypic variation and if such manipulations lead to detectable changes in fitness. This research forms a framework for understanding how fitness function and representation interact to produce evolvability. Ultimately evolvable encodings may lead to evolutionary algorithms that exhibit the structured complexity and robustness found in nature.