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Evolutionary Algorithms in Theory and Practice. 1.1 Biological Background. 발표자 : 김정집. 1.Organic Evolution and Problem Solving. interdisciplinary research field biology, artificial intelligence, numerical optimization, and decision support organic evolution - PowerPoint PPT Presentation
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1.1 Biological Background
발표자 : 김정집
Evolutionary Algorithms in Theory and Practice
1.Organic Evolution and Problem Solving
interdisciplinary research field biology, artificial intelligence, numerical
optimization, and decision support organic evolution
collective learning process within a population of individuals
individual a search point container of current knowledge about the “laws” of the
environment
fitness value, recombination, mutation, and selection
Different Mainstreams
three different Mainstreams Evolution Strategies (ESs) Genetic Algorithms (GAs) Evolutionary Programming(EP)
Index sec 1.1 biological background sec 1.2 impact on AI, and ML sec 1.3 a global optimization algorithm as random
search algorithm sec 1.4 overview of the history of Eas
1.1 Biological Background
Darwinian theory of evolution(Charles Darwin) natural selection mutation on phentypes
-> selection under limited environmental conditions
-> advantageous organisms survives
Neodarwinism
synthetic theory of evolution genes
transfer units of heredity changed by mutations
population evolving unit consists of a common gene pool
indirect fitness natural selection as no active driving force What is mapping from genotype to phenotype?
Adaptation
denotes a general advantage in ecological or physiological efficiency nongenetic-somatic adaptation genetic adaptation
“To What” any major kind of environment (adaptive zone) ecological niche ( the set of possible environments
that permit survival of a species)
Adaptive surface
possible biological trait combinations natural analogy to the optimization problem climbing the hill nearest to the starting point
genetic drift random decrease or increase of biological trait
frequencies
dynamically changing by means of environment-population interactions
Schematic diagram of an adaptive surface
1.1.1 Life and Information Processing
DNA: 2strands nucleotide base
Adenine(A), Thymine(T), Cytosine(C), Guanine(G)
purine base (A or G) pyrimidine base ( T or C)
creates the phenotype from the genotype
protein biosynthesis mapping genotype to phenotype
polygeny - m:1 pleiotropy - 1:m epistasis
alphabet of amino acids : 20 different one mRNA(1 strand):transcription,nucleus->riboso
mes tRNA:translation in ribosome
the genetic code
protein biosynthesis
central dogma of molecular genetics
DNA->RNA->Protein the proof of the incorrectness of Lamarckism
Hierarchy of the genetic information
1.1.2 Meiotic Heredity
mitosis cell division with identical genetic material
phylogeny(evolution) meiotic cell division
Meiosis(I)
Meiosis(II)
crossover
position(s) at random in nature, 1~8 points haploid case(*)
haploid gameter->diploid zygote->haploid cell recombination and mutaion occur in zygote
one-point crossover
characteristics of meiosis
1.1.3 mutations
DNA-replication is overwhelmingly exact but not perfect
for a specific gene of the human genome, Pm=6*10-6~8*10-6
by origin normal-in the replication process exogenous factors
classes of mutations
by location somatic generative
usual deviations gene mutations chromosome mutations genome mutations
gene , genome mutations
gene mutations small mutations
little variation-do not negatively effect
large mutations cause phenotype deviations
progressive(constructive) mutations cause crossings of boundaries between species
genome mutations not been tested as an extension of EAs
chromosome mutations
losses of chromosome regions deficiencies and deletions
doubling of chromosome regions duplications
reorganization of chromosomes translocations and inversions
terminal and internal segment losses
duplication event
inversion event
1.1.4 Molecular Darwinism
human genome consists of one billion nucleotide bases 4^1,000,000,000 possibilities random emergence of self-reproducing units can be
called impossible explain the efficiency of biological evolution
necessary conditions for Darwinian selection
Metabolism Self-reproduction Mutation
Eigen’s equations
Eigen’s equations for the dynamical behavior of species
: build-up term resulting from self-replication : term incorporating destruction : transition probability from class k to I
: growth and shrinking processes of
the total number of individuals
Under the assumption of a constant overall organization buffering the concentrations Ai of energy-rich subs
tances, s.t. AiQi=const total size of the system is limited excess productivity
excess productivity must be compensated by transportation through the flow
Average excess productivity
Eigen’s eq. Can be transferred to
where , selective value of a species I
only those species having Wi above E(t) will grow the number shifting E(t) to an optimum
representing Maximum selective value of all species
The selection criterion allow growing of a new species m to become the
dominant one
the quasi-species the currently dominant species together with its stationary
distribution of mutants emerging from this species
A maximum length s.t. the information can be preserved by reproduction
the ratio of the wild-type(dominant species) reproduction rate to the average reproduction rate of the rest
Experimental results lmax is no longer than hundred nucleotide bases
Darwinian selection N=kN In principle, any new species can grow and become
the dominant ones. Eigen’s concept of a hypercycle
N=kN2 does not allow for diversity of species
Summary of experiments
Summary of experiments coexistent evolution according to the principle of D
arwiniam selection Hypercyclic system stabilized. Hypercuclic selection optimizes the system. Only o
ne universal genetic code is produced The first biological cells emerge Darwiniam evolution leads to the development of t
he known variety of species
Using a birth and death model, an approximate analytical expression for the dependence of the error threshold
more approximated form