CN109584953A - A kind of biological differentiation method based on cellular automata - Google Patents
A kind of biological differentiation method based on cellular automata Download PDFInfo
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- CN109584953A CN109584953A CN201811332831.3A CN201811332831A CN109584953A CN 109584953 A CN109584953 A CN 109584953A CN 201811332831 A CN201811332831 A CN 201811332831A CN 109584953 A CN109584953 A CN 109584953A
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Abstract
The biological differentiation method based on cellular automata that the invention discloses a kind of, comprising: obtain the characteristic of biology, determine the required time for developing biology, form impact factor；According to the characteristic of biology, biological shapes region is generated by bionics algorithm simulation；The weight that each impact factor is obtained by step analysis algorithm divides biological shapes region according to the weight size proportional assignment of each factor；Read group total is weighted to all impact factors according to the weight size of variable and develops suitability data；Tissue growth pattern is selected, simulated tissue growth is carried out to biological shapes region corresponding after division in conjunction with suitability data are developed；After the time required to meeting preset differentiation, stop simulation process, exports biology and develop result；The method of the present invention solution is not bound with the technical issues of unstable factor develops biology in the prior art, and then realization reduces error rate to the utmost, realizes biological evolution process.
Description
Technical field
The present invention relates to biological data process field more particularly to a kind of biological differentiation sides based on cellular automata
Method.
Background technique
Evolutionism is most basic one of the theory of biology, and evolution refers to that biology is acted in variation, heredity and natural selection
Under evolving development, species eliminate and species generate process；Originally without life on the earth, about before more than 30 hundred million years, certain
Under conditions of, primitive life is formd, thereafter, biology is continuous to evolve, and there is more than 170 ten thousand objects in the world up to today
Kind.Therefore, biological evolution process is studied all to be of great significance to the mankind, society.
In the prior art, the effective volume and free volume concept for introducing biomolecule drill biological evolution
The considerations of becoming, but being established due to traditional differentiation method and developed in the case where extreme is stablized, lacked to unstable factor,
Such as influence the influence of the time and biological self attributes of biological evolution to evolution process, in actually evolving, these influence because
The presence of son will have a direct impact on the evolutionary process of biology, cause differentiation result in the prior art and actual result to have larger
Difference.
Summary of the invention
The biological differentiation method based on cellular automata that the present invention provides a kind of, to solve to be not bound in the prior art
The technical issues of unstable factor develops biology so as to closer to reality develops biological evolution, in turn
Realization reduces error rate to the utmost, realizes biological evolution process.
In order to solve the above-mentioned technical problem, the biological differentiation side that the embodiment of the invention provides a kind of based on cellular automata
Method, comprising:
The characteristic of biology is obtained, determines the required time for developing the biology, forms impact factor；
According to the characteristic of the biology, biological shapes region is generated by bionics algorithm simulation；
The weight that each impact factor is obtained by step analysis algorithm is divided in proportion according to the weight size of each factor
With the division biological shapes region；
Read group total is weighted to all impact factors according to the weight size of variable and develops suitability data；
Tissue growth pattern is selected, biological shapes region corresponding after division is carried out in conjunction with the development suitability data
Simulated tissue increases, and after meeting the time required to preset differentiation, stops simulation process, exports biology and develops result.
Preferably, the biological differentiation method, further includes:
Spontaneous growth pattern is selected, presetting a certain shape area is that seed region is iterated simulation to periphery region
Increase, the time required to meeting preset differentiation after, stop simulation process, export biology develop result.
Preferably, the spontaneous growth pattern of selection, presetting a certain shape area is seed region to periphery institute
Simulation is iterated in region to increase, comprising:
Spontaneous growth pattern is selected, randomly chooses the biological shapes region of a certain piece of division as seed Development area；
Simulated tissue is carried out as Development area using the neighboring area of the seed Development area to increase；
Qualitative to the region after progress simulated tissue growth is seed region, and carries out next wheel model to the region on its periphery
Quasi- tissue increases.
Preferably, the characteristic for obtaining biology, determines the required time for developing the biology, comprising:
It determines the biological object of research, and collects all biological characteristics for integrating the biological object, including dynamic is raw
Object feature and static biological characteristic；
The evolution time of the biological object is determined according to the actual conditions of research；
The dynamic biological feature that will acquire and the static biological characteristic formed in conjunction with the evolution time influence because
Son storage in the database, facilitates data to extract.
Preferably, the characteristic according to the biology generates biological shape by bionics algorithm simulation
Shape region, further includes: the characteristic of the biology is pre-processed, the excessive data of error are deleted.
Preferably, the pretreatment includes the data for deleting numerical exception, deletes duplicate data and unified institute
There is the format of data.
Preferably, the selection tissue growth pattern, in conjunction with the development suitability data to corresponding after division
Biological shapes region carry out simulated tissue growth, comprising:
Tissue growth pattern is selected, the development suitability data are extracted in the tissue growth pattern；
Development suitability data corresponding to each biological shapes Region Matching of division；
According to the value for developing suitability data, simulated tissue growth is successively carried out from big to small.
Preferably, the bionics algorithm includes genetic algorithm.
Preferably, the calculating develops suitability data, are as follows:
pg=b1x1+b2x2+…+bkxk+…+bnxn
In formula, xkIt is k-th of impact factor, bkIt is variable xkWeight, weight size calculated by analytic hierarchy process (AHP)
It arrives, pgTo develop suitability data, n is impact factor quantity.
Preferably, the biological differentiation method, further includes: in carrying out evolution process, virtual environment temperature is set
Degree is at 10 degrees Celsius between 36 degrees Celsius.
Compared with the prior art, the embodiment of the present invention has the following beneficial effects:
Biological data is handled by cellular automata, is drilled in conjunction with the impact factor in practical evolutionary process
Become, solution is not bound with the technical issues of unstable factor develops biology in the prior art, so as to closer to reality
Ground develops biological evolution, and then realization reduces error rate to the utmost, realizes biological evolution process.
Detailed description of the invention
Fig. 1: for the specific steps flow diagram in the embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Fig. 1 is please referred to, the preferred embodiment of the present invention provides a kind of biological differentiation method based on cellular automata, packet
It includes:
S1 obtains the characteristic of biology, determines the required time for developing the biology, forms impact factor；
S2 generates biological shapes region by bionics algorithm simulation according to the characteristic of the biology；
S3 obtains the weight of each impact factor by step analysis algorithm, according to each factor weight size press than
Example distribution divides the biological shapes region；
S4 is weighted read group total to all impact factors according to the weight size of variable and develops suitability data；
S5 selects tissue growth pattern, in conjunction with the development suitability data to biological shapes region corresponding after division
Carry out simulated tissue growth, the time required to meeting preset differentiation after, stop simulation process, export biology develop result.
Biological data is handled by cellular automata, is drilled in conjunction with the impact factor in practical evolutionary process
Become, solution is not bound with the technical issues of unstable factor develops biology in the prior art, so as to closer to reality
Ground develops biological evolution, and then realization reduces error rate to the utmost, realizes biological evolution process.
In the present embodiment, the biological differentiation method, further includes:
S6 selects spontaneous growth pattern, and presetting a certain shape area is that seed region is iterated periphery region
Simulation increases, and after meeting the time required to preset differentiation, stops simulation process, exports biology and develops result.
Biology is developed and can more pointedly be developed to biological feature by spontaneous growth pattern, with group
Knit unlike growth pattern, tissue growth pattern be to the differentiation of biology it is unified, comprehensive, all biological datas are carried out
It develops, and spontaneous growth pattern is to put and be developed to face, from part to being developed comprehensively.
In the present embodiment, the spontaneous growth pattern of selection, presetting a certain shape area is seed region to periphery institute
Simulation is iterated in region to increase, comprising:
Spontaneous growth pattern is selected, randomly chooses the biological shapes region of a certain piece of division as seed Development area；
Simulated tissue is carried out as Development area using the neighboring area of the seed Development area to increase；
Qualitative to the region after progress simulated tissue growth is seed region, and carries out next wheel model to the region on its periphery
Quasi- tissue increases.
Under spontaneous growth pattern, determine that seed region develops biology, effect can make the biological characteristic
It is more prominent, for the not high characteristic area of attention rate, such as capped hair etc., it is light to be set as neighboring area progress
Change, the feature of prominent seed region.
In the present embodiment, the characteristic for obtaining biology, determines the required time for developing the biology, comprising:
It determines the biological object of research, and collects all biological characteristics for integrating the biological object, including dynamic is raw
Object feature and static biological characteristic；
The evolution time of the biological object is determined according to the actual conditions of research；
The dynamic biological feature that will acquire and the static biological characteristic formed in conjunction with the evolution time influence because
Son storage in the database, facilitates data to extract.
Dynamic biological feature and static biological characteristic in conjunction with biology, cover all around the high dimensional data of biology,
In conjunction with the evolution time that actual conditions determine, it can more optimize subsequent evolution process, so that it is more ideal to develop result.
In the present embodiment, the characteristic according to the biology generates biological shape by bionics algorithm simulation
Shape region, further includes: the characteristic of the biology is pre-processed, the excessive data of error are deleted.
Error information is deleted, subsequent evolution process is advanced optimized.
In the present embodiment, the pretreatment includes the data for deleting numerical exception, deletes duplicate data and unified institute
There is the format of data.
Data are compared with ideal data value first, the too large or too small abnormal data of numerical value is deleted,
Then duplicate data are also deleted, reduces and calculates the time, improved operational efficiency, be finally the format of uniform data, can keep away
Error caused by exempting from data in the operation.
In the present embodiment, the selection tissue growth pattern, in conjunction with the development suitability data to corresponding after division
Biological shapes region carry out simulated tissue growth, comprising:
Tissue growth pattern is selected, the development suitability data are extracted in the tissue growth pattern；
Development suitability data corresponding to each biological shapes Region Matching of division；
According to the value for developing suitability data, simulated tissue growth is successively carried out from big to small.
The process of tissue growth pattern is further refined, team's data are arranged from big to small, match corresponding biology
Region carries out tissue growth by the data of biology itself, so that developing more comprehensively, effect is more preferable.
In the present embodiment, the bionics algorithm includes genetic algorithm.
In the present embodiment, the calculating develops suitability data, are as follows:
pg=b1x1+b2x2+…+bkxk+…+bnxn
In formula, xkIt is k-th of impact factor, bkIt is variable xkWeight, weight size calculated by analytic hierarchy process (AHP)
It arrives, pgTo develop suitability data, n is impact factor quantity.
In the present embodiment, the biological differentiation method, further includes: in carrying out evolution process, virtual environment temperature is set
Degree is at 10 degrees Celsius between 36 degrees Celsius.
Environment temperature in biological growth or evolutionary process is simulated, the too low cell that will cause of temperature is cooling, and temperature is excessively high to be made
It is free at cellular elements, it is unfavorable for being developed, therefore, the present embodiment has selected 10 degrees Celsius of temperature ranges for arriving 36 degrees Celsius
Biological growth or evolutionary process are simulated.
The present invention is handled biological data by cellular automata, in conjunction with the impact factor in practical evolutionary process into
Row develop, solution be not bound with the technical issues of unstable factor develops biology in the prior art, so as to close to
Realistically biological evolution is developed, and then realization reduces error rate to the utmost, realizes biological evolution process.
Particular embodiments described above has carried out further the purpose of the present invention, technical scheme and beneficial effects
It is described in detail, it should be understood that the above is only a specific embodiment of the present invention, the protection being not intended to limit the present invention
Range.It particularly points out, to those skilled in the art, all within the spirits and principles of the present invention, that is done any repairs
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of biological differentiation method based on cellular automata characterized by comprising
The characteristic of biology is obtained, determines the required time for developing the biology, forms impact factor；
According to the characteristic of the biology, biological shapes region is generated by bionics algorithm simulation；
The weight that each impact factor is obtained by step analysis algorithm is drawn according to the weight size proportional assignment of each factor
Divide the biological shapes region；
Read group total is weighted to all impact factors according to the weight size of variable and develops suitability data；
Tissue growth pattern is selected, biological shapes region corresponding after division is simulated in conjunction with the development suitability data
Tissue increases, and after meeting the time required to preset differentiation, stops simulation process, exports biology and develops result.
2. the method as described in claim 1, which is characterized in that the biology differentiation method, further includes:
Spontaneous growth pattern is selected, presetting a certain shape area is that seed region is iterated simulation increasing to periphery region
It is long, after meeting the time required to preset differentiation, stop simulation process, exports biology and develop result.
3. method according to claim 2, which is characterized in that the spontaneous growth pattern of selection presets a certain shape area
Simulation is iterated to periphery region for seed region to increase, comprising:
Spontaneous growth pattern is selected, randomly chooses the biological shapes region of a certain piece of division as seed Development area；
Simulated tissue is carried out as Development area using the neighboring area of the seed Development area to increase；
Qualitative to the region after progress simulated tissue growth is seed region, and carries out next round simulation group to the region on its periphery
Knit growth.
4. the method as described in claim 1, which is characterized in that the characteristic for obtaining biology determines and develops the life
The required time of object, comprising:
It determines the biological object of research, and collects all biological characteristics for integrating the biological object, including dynamic biological is special
It seeks peace static biological characteristic；
The evolution time of the biological object is determined according to the actual conditions of research；
The dynamic biological feature that will acquire and the static biological characteristic are in conjunction with evolution time composition impact factor storage
It deposits in the database, data is facilitated to extract.
5. the method as described in claim 1, which is characterized in that the characteristic according to the biology passes through bionics
Algorithm simulation generates biological shapes region, further includes: pre-processes to the characteristic of the biology, it is excessive to delete error
Data.
6. the method as described in claim 1, which is characterized in that the pretreatment includes the data for deleting numerical exception, deletes
The format of duplicate data and unified all data.
7. the method as described in claim 1, which is characterized in that the selection tissue growth pattern is suitable in conjunction with the development
Property data to biological shapes region corresponding after division carry out simulated tissue growth, comprising:
Tissue growth pattern is selected, the development suitability data are extracted in the tissue growth pattern；
Development suitability data corresponding to each biological shapes Region Matching of division；
According to the value for developing suitability data, simulated tissue growth is successively carried out from big to small.
8. the method as described in claim 1, which is characterized in that the bionics algorithm includes genetic algorithm.
9. the method as described in claim 1, which is characterized in that the calculating develops suitability data, are as follows:
pg=b1x1+b2x2+…+bkxk+…+bnxn
In formula, xkIt is k-th of impact factor, bkIt is variable xkWeight, weight size is calculated by analytic hierarchy process (AHP), pgFor
Develop suitability data, n is impact factor quantity.
10. the method as described in claim 1, which is characterized in that the biology differentiation method, further includes: developed
Virtual environment temperature is arranged at 10 degrees Celsius between 36 degrees Celsius in Cheng Zhong.
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