A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Learning decision trees using the Fourier spectrum
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The harmonic sieve: a novel application of Fourier analysis to machine learning theory and practice
The harmonic sieve: a novel application of Fourier analysis to machine learning theory and practice
A tractable Walsh analysis of SAT and its implications for genetic algorithms
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Further Experimentations on the Scalability of the GEMGA
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
The behavior of adaptive systems which employ genetic and correlation algorithms
The behavior of adaptive systems which employ genetic and correlation algorithms
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Empirical studies of the genetic algorithm with noncoding segments
Evolutionary Computation
Predicting epistasis from mathematical models
Evolutionary Computation
Scalability problems of simple genetic algorithms
Evolutionary Computation
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Gene expression evaluates the genetic fitness of an organism through a sequence of representation transformations (DNA→mRNA→Protein). Moreover it does so in a very distributed and decomposed fashion by evaluating different portions of the DNA in order to produce various proteins in different body cells. This chapter reviews some of the recent results that underscore the possible critical role of gene expression in scalable genetic search. It considers a Fourier basis representation to analyze genetic fitness functions and shows that polynomial-time construction of a decomposed representation in the Fourier basis is possible when the function has a polynomial-size description. It also points out that genetic code-like transformations may offer us a unique technique to transform some functions of exponential description in the Fourier basis to an exponentially long representation with only a polynomial number of terms that are exponentially more significant than the rest. This may be useful for a polynomial-time approximation of an exponential description. Since the construction of decomposed representation of functions from observed data plays an important role in machine learning, data mining, and black-box optimization, the role of gene expression in scalable genetic search appears quite critical.