Communications of the ACM
Computational limitations on learning from examples
Journal of the ACM (JACM)
A general lower bound on the number of examples needed for learning
Information and Computation
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
SIAM Journal on Computing
On learning ring-sum-expansions
SIAM Journal on Computing
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
An introduction to computational learning theory
An introduction to computational learning theory
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Theoretical Aspects of Evolutionary Algorithms
ICALP '01 Proceedings of the 28th International Colloquium on Automata, Languages and Programming,
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Evolvability from learning algorithms
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Gene Expression Programming Based on Subexpression Library and Clonal Selection
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Fixed-parameter evolutionary algorithms and the vertex cover problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On evolvability: the swapping algorithm, product distributions, and covariance
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Evolvability via the Fourier transform
Theoretical Computer Science
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Living organisms function according to complex mechanisms that operate in different ways depending on conditions. Evolutionary theory suggests that such mechanisms evolved as result of a random search guided by selection. However, there has existed no theory that would explain quantitatively which mechanisms can so evolve in realistic population sizes within realistic time periods, and which are too complex. In this paper we suggest such a theory. Evolution is treated as a form of computational learning from examples in which the course of learning is influenced only by the fitness of the hypotheses on the examples, and not otherwise by the specific examples. We formulate a notion of evolvability that quantifies the evolvability of different classes of functions. It is shown that in any one phase of evolution where selection is for one beneficial behavior, monotone Boolean conjunctions and disjunctions are demonstrably evolvable over the uniform distribution, while Boolean parity functions are demonstrably not. The framework also allows a wider range of issues in evolution to be quantified. We suggest that the overall mechanism that underlies biological evolution is evolvable target pursuit, which consists of a series of evolutionary stages, each one pursuing an evolvable target in our technical sense, each target being rendered evolvable by the serendipitous combination of the environment and the outcome of previous evolutionary stages.