CN108960486B - Interactive set evolution method for predicting adaptive value based on gray support vector regression - Google Patents
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Abstract
The invention discloses an interactive set evolution calculation method based on a prediction adaptive value of a gray support vector regression machine, which comprises the following specific steps of: before the evolution starts, a system provides a design environment for a user and randomly generates an initial evolution population; through an interactive interface, the person carries out numerical evaluation on the Nc individuals; the system clusters the population according to the individual similarity, estimates the adaptive value of the non-user evaluation individual in the cluster, and adopts a gray support vector regression machine to predict the adaptive value; carrying out Pareto dominance ordering on the ensemble evolved individuals according to diversity and distributivity uncertainty measures, and generating an equal-scale temporary population by adopting self-adaptive intersection and variation operations on the ensemble evolved individuals; combining the parent population and the temporary population, and selecting the first N individuals as child populations; and finally, dividing the sub-generation population into Nc units at equal intervals, randomly selecting 1 individual from each unit, and recommending the Nc individuals to the user.
Description
Technical Field
The invention belongs to the field of intelligent calculation, and particularly relates to an interactive set evolution optimization method based on a grey support vector regression prediction adaptive value, which is used for type selection of a color matching scheme.
Background
The application of interactive evolutionary computation based on heuristic learning, which was proposed in the 90 s of the 20 th century, to solving the implicit index optimization problem requires 2 basic problems to be solved: (1) how to effectively extract implicit knowledge; (2) how to solve implicit performance indicators with high quality.
For the 1 st problem, there are mainly 2 research strategies: firstly, under the mode of simultaneous interaction and evolution, implicit knowledge is directly extracted through a human-computer interaction interface. The method mainly focuses on the research of the adaptive value assignment mode, for example, in the 2 nd phase of the journal "automatic journal" published in 2014, "the interval adaptive value interactive genetic algorithm-based weighted multi-output gaussian process proxy model" adopts uncertain numbers such as interval numbers to express the adaptive values, and reflects the preference characteristics of the user; a journal published in 2017, namely ' electronic journal ', No. 12 ' non-user adaptive value interactive genetic algorithm based on entropy maximization criterion ' and a journal published in 2016, namely ' Applied Intelligence ', No. 3 ' Predicting user's preference using neural networks and psychology models ' are combined with a user browsing behavior to express personalized requirements, so that the defect of preferential knowledge expression of numerical type adaptive values is overcome. The method has the advantages of small calculation amount for directly acquiring the implicit knowledge, simplicity, strong subjectivity, larger information uncertainty and higher noise content. And secondly, implicit knowledge is indirectly acquired, namely the implicit knowledge is mined in the evolution process, and valuable preference information is extracted. Journal published in 2017, "zheng zhou university press (engineering edition)", interactive genetic algorithm based on possibility condition preference network and application thereof "fit user preferences using preference network, estimate individual fitness value and guide search; similarly, the journal published in 2017, automatic Software Engineering, 3 rd "An Architecture based on interactive optimization and machine learning applied to the next release project" learns the user preferences using a neural network to estimate the adaptation value. The indirect acquisition of implicit knowledge is deep excavation of preference information, so that the searching capability of an algorithm is enhanced, but the modeling of an agent model is complex, estimation errors cannot be measured, and a large amount of adaptive value noise is still brought. The adaptive value estimation/expression strategy for deeply mining the user behavior mode is expected to improve the effect of implicit knowledge extraction or extract knowledge in a more complex scene, but from the overall optimization performance, the performance improvement of the algorithm obtained only by improving the implicit knowledge extraction strength is limited. For the 2 nd problem, it is mainly realized by an efficient evolution strategy to reduce user fatigue. The algorithm searching capability can be improved by adopting a large population scale, but the method needs to solve the problem of estimating the adaptation values of a large number of unexevated individuals. The agent model is adopted to execute the evolution operation, so that the workload of the user can be reduced, and the time overhead can be saved for the user. For example, in the publication of 2014, journal "electronic journal" at stage 8, "interactive genetic algorithm for selecting evolved individuals based on elite sets" individual elite sets are constructed, and individual categories similar to the elite sets are selected for genetic operation directly, so that the burden of users is reduced. The idea is combined, and the performance of the algorithm can be obviously improved by adopting the evolution agent model under large-scale population.
In the algorithm, how to design a population evolution strategy is crucial, and since the proxy model generates error accumulation, the control error is a problem which is difficult to solve. If the set evolution method is applied to large-scale population cluster evolution, the search efficiency can be improved; meanwhile, if the estimated adaptive value of the existing large-scale population is re-evaluated by adopting the agent model, the accuracy of the adaptive value can be improved, and the two are fused to make up for the deficiency, so that the performance of the current interactive evolution optimization algorithm is expected to be improved. By looking up relevant documents, an interactive set evolution optimization method for predicting adaptive values by using a gray support vector regression does not exist at present. If a related efficient design system can be developed, the color matching design can be promoted, and the method has great instructive significance for other product designs.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the interactive evolution optimization method overcomes the defects of the existing adaptive value estimation technology, reduces adaptive value estimation errors, reduces user burden, enhances algorithm searching capability and improves evolution optimization quality.
The technical scheme of the invention is as follows:
firstly, a large-scale population expansion search space is adopted, and population clustering is carried out by taking a limited number of user evaluation individuals as a clustering center. Then, estimating an adaptive value of the non-clustering center individuals in the clusters according to the individual similarity of the integrated individual browsing behaviors, and further predicting the individual adaptive value by adopting a gray support vector regression model to form a set evolution individual. And finally, realizing a set evolution algorithm under an NSGA-II paradigm by adopting a set evolution strategy and self-adaptive cross mutation operation. In order to implement the present invention, 2 main problems of individual fitness value prediction and set evolution strategy design need to be solved.
(1) Adaptive value prediction for gray support vector machine
The exact number of individuals is adapted to the value f (x)k(t))，xk(t) e x (t) is denoted as F0＝(f0(x1(t)),f0(x2(t)),…,f0(xN(t))) constituting an original sequence of individual fitness values. Then F0The 1-AGO sequence of (A) is F1＝(f1(x1(t)),f1(x2(t)),…,f1(xN(t)))，
In order to make the influence weight of the original data on the objective function consistent in each feature dimension, the original sequence is firstly normalized:
the normalized data sequence is F0'＝(f0'(x1(t)),f0'(x2(t)),…,f0'(xN(t))). Then, F is established0' 1-AGO sequence F1＝(f1'(x1(t)),f1'(x2(t)),…,f1'(xN(t)))，
The gray support vector model is then noted as:
in the formula: b is a bias term; ω is the weight. 1-AGO transformation term f1'(xk(t)) represents a non-linear mapping of the individual fitness value input space to the high-dimensional feature space. The unknown parameters ω and b in the equation are estimated from the training set in the high-dimensional feature space using the ε -insensitive loss function proposed by Vapnik:
due to minimization
The minimum deviation of epsilon can be guaranteed, and the gray support vector model can be written as the following convex optimization problem:
in the formula:
controlling the fitting precision of the model; and C is a regularization constant and controls the punishment degree of the samples exceeding the error. The above equation can be solved by Lagrange multiplier method:
a dual optimization model can be obtained:
in the formula < f1'(xi(t)),f1'(xj(t)) > represents the vector inner product. The invention selects a kernel function K (f)1'(xi(t)),f1'(xj(t)))＝exp(-υ||f1'(xi(t)),f1'(xj(t)) |), upsilon > 0, upsilon is a nuclear parameter. Substituting the kernel function into the above formula to obtain alphak,
And b, the gray support vector regression model is:
in the formula (I), the compound is shown in the specification,
is for the newly input individual adaptation value f1'(xnew(t)) the gray support vector machine predictor.
And finally, restoring the predicted value to the original sequence scale to obtain:
clustering population individuals and predicting adaptive value to form individual subclass x1(t),x2(t),…,xNc(t) is a set of individuals, and it is natural to consider the subclass of individuals as evolved individuals (set evolved individuals) based on the idea of set evolution.
(2) Ensemble evolution strategy
By selecting a proper performance index, the implicit performance index optimization can be converted into the following general set decision variable optimization problem:
max F(X)＝(F1(X),F2(X),…,FId(X))
wherein the content of the first and second substances,
as powers of decision space SCollecting; x ═ X1(t),x2(t),…,xNc(t) } is a population formed by the evolved individuals; fd(X), d ═ 1,2, …, Id is the performance index for population X; id is the dimension of the post-transformation optimization problem and is much smaller than Nc. In combination with the implicit performance index characteristics, this section gives a new set individual comparison measure, as follows.
The j evolution individual of the t generation evolution population x (t) is recorded as
Wherein x isj(t) as a central individual, and an individual fitness value of
M＝|xj(t) | is the evolved individual xj(t) number of individuals involved.
The diversity is characterized by the aid of the entropy of the similarity information of the evolved individuals:
in the above formula, the first and second carbon atoms are,
wherein x isirR is 1,2, …, g is the r attributes that make up the individual,
is xirThe attribute value of (2). x is the number ofj(t) center individual xj(t) Cluster center similarity μ (x) with other evolved individualsi(t),xj(t)) the larger, xj(t) the more similar the other evolved individuals, the poorer the population diversity, in this case, F2The smaller (X) is. Otherwise, F2The larger (X) is, the looser the evolution individuals of the population are, and the better the diversity of the population is.
The distribution was characterized using the following formula:
in the formula, d (x)j(t)) is xj(t) a minimum crowding distance of,
d*the average crowding distance of individuals in population evolution,
for the evolved individuals with the same sequence value, further adopting interval gray number gray scale to depict uncertainty:
the above formula will evolve the individual xjThe adaptive value of (t) is regarded as the interval gray number, xjThe uncertainty of the evaluation of (t) can be characterized by interval gray number gray scale, i.e. the smaller gf (x), the less uncertainty of the evolving individual, and vice versa.
When x is1||sparx2When x is selected to be arg min { GF (x)1),GF(x2) The winning individual. By the method, the advantages and the disadvantages of any 2 set evolution individuals of the population can be compared.
(3) Adaptive set intersection and mutation probabilities
Aiming at the characteristics of set evolution, the subsection gives self-adaptive set intersection and mutation operators. For the cross probability in the same evolution individual, adopting the self-adaptive cross probability:
the above formula considers the relationship between the cross operation and the diversity and uncertainty change of the set evolution individuals in the evolution process. For enhancing the searching capability, the uncertainty of the evolved individuals is in direct proportion to the cross probability; in order to keep the dominant individual, the diversity of the evolved individual is in inverse proportion to the cross probability, the overall cross probability is gradually reduced along with the evolution, and the convergence of the algorithm is enhanced; and vice versa.
The mutation operation of the ensemble evolution individuals adopts a single point mutation mode, and the self-adaptive mutation probability is Pm 1And Pm 2The part 2 comprises:
the above formula reflects the relationship between the diversity and convergence of the mutation operation and the evolved individuals. In order to preserve the dominant individual, the diversity and convergence should be inversely proportional to the variation probability, and vice versa.
In the formula
Is an evolved individual xj(t) individuals to be mutated
The adaptive value of (a). The above expression reflects the relationship between mutation operation and the adaptive value of the evolved individual. I.e. selecting the evolved individual x of the variation operation to protect the dominant individual from being damagedjIn (t), the larger the adaptation value, the smaller the probability of mutation being performed.
Self-adaptive mutation probability P of evolved individualsmComprises the following steps:
the color individual coding method comprises the following steps: the RGB color mode obtains various colors through the change of three color attributes of red (R), green (G) and blue (B) and the superposition of the three color attributes, and the value range of each color attribute is 0-255. The RGB color individual chromosome adopts binary coding, the coding length is 24 bits, wherein the first 8 bits represent red attributes, the middle 8 bits represent green attributes, the last 8 bits represent blue attributes, and the corresponding binary coding range of each color attribute is 00000000-11111111. The optimization target is a certain preset target color, and through evolutionary optimization, a user obtains a color individual matched with the target color.
The invention has the advantages and positive effects that:
1. according to the method, the grey support vector machine is adopted to predict the non-user evaluation individual adaptive value, compared with other adaptive value estimation strategies, the estimation error is greatly reduced, and meanwhile, the user fatigue is reduced;
2. the invention adopts a new set evolution strategy to realize evolution, obviously improves the search efficiency and the optimization quality, and obtains good optimization effect when being applied to the color matching problem.
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FIG. 1 is a general flow diagram of the present invention;
FIG. 2 is a system interface diagram of the present invention;
FIG. 3 is a comparison of search times according to the present invention;
FIG. 4 is a comparison diagram of evolution algebra according to the present invention.
Detailed Description
The following further detailed description of the embodiments of the present invention is made with reference to fig. 1-4, and the following examples are illustrative only, not limiting, and are not intended to limit the scope of the present invention.
An interactive set evolution calculation flow based on the prediction adaptive value of a gray support vector regression is shown in fig. 1. The method comprises the following steps:
The color individual coding method comprises the following steps: the RGB color mode obtains various colors through the change of three color attributes of red (R), green (G) and blue (B) and the superposition of the three color attributes, and the value range of each color attribute is 0-255. The RGB color individual chromosome adopts binary coding, the coding length is 24 bits, wherein the first 8 bits represent red attributes, the middle 8 bits represent green attributes, the last 8 bits represent blue attributes, and the corresponding binary coding range of each color attribute is 00000000-11111111. The optimization target is a certain preset target color, and through evolutionary optimization, a user obtains a color individual matched with the target color.
And 2. step 2.
First, a partial number of individuals x are selectedi(t), i is 1,2, … Nc as the individual clustering centers, and the adaptive value f (x) is evaluated by the useri(t)). Then, other non-central individuals x in the population are calculated one by one according to the formulao(t) the similarity between the individual and each central individual, clustering the individual according to the similarity, and marking as xi(t)＝{xi(t) }, i ∈ {1,2, … Nc }. Thus, the population x (t) is finally divided into Nc subclasses, denoted as x1(t),x2(t),…,xNc(t) of (d). Then, a weighted average of the similarity to the center of each cluster is obtained in each individual subclass, and the non-center individual x is estimatedo(t) an adaptation value. To reduce the error of the adaptive value estimation, theNon-central individual estimation adaptation value f (x)o(t)) adopting a gray support vector regression machine to predict to obtain a non-central individual final adaptive value for subsequent evolution
And 3. step 3.
And sequentially calculating diversity, distribution and uncertainty measures for the set evolution individuals.
Measure of diversity:
measure of distribution:
in the formula, d (x)j(t)) is xj(t) a minimum crowding distance of,
d*the average crowding distance of individuals in population evolution,
uncertainty measure:
the above formula will evolve the individual xjThe adaptive value of (t) is regarded as the interval gray number, xjThe uncertainty of the evaluation of (t) can be characterized by interval gray number gray scale, i.e. the smaller gf (x), the less uncertainty of the evolving individual, and vice versa.
When x is1||sparx2When x is selected to be arg min { GF (x)1),GF(x2) The winning individual. By the method, the advantages and the disadvantages of any 2 evolved individuals of the population can be compared.
And 4. step 4.
And (4) generating the temporary population with the same scale by adopting self-adaptive crossover and mutation operations on the ensemble evolved individuals. Self-adaptive cross probability:
the above formula considers the relationship between the cross operation and the diversity and uncertainty change of the set evolution individuals in the evolution process. For enhancing the searching capability, the uncertainty of the evolved individuals is in direct proportion to the cross probability; in order to keep the dominant individual, the diversity of the evolved individual is in inverse proportion to the cross probability, the overall cross probability is gradually reduced along with the evolution, and the convergence of the algorithm is enhanced; and vice versa.
The mutation operation of the set evolution individuals adopts a single point mutation mode, and the probability of the self-adaption mutation is determined by
And
the part constitutes:
the above formula reflects the relationship between the mutation operation and the diversity and distribution of the evolved individuals. In order to preserve the dominant individual, the diversity and distribution should be inversely proportional to the probability of variation, and vice versa.
In the formula
Is an evolved individual xj(t) individuals to be mutated
The adaptive value of (a). To protect dominant individuals from being destroyed, evolved individuals x of mutation operation are selectedjIn (t), the larger the adaptation value, the smaller the probability of mutation being performed.
Self-adaptive mutation probability P of evolved individualsmComprises the following steps:
and 5. step 5.
Merging the parent population and the temporary population, sequencing the merged population, and selecting the first N individuals as child populations; and finally, dividing the sub-generation population into Nc units at equal intervals, randomly selecting 1 individual from each unit, and recommending the Nc individuals to the user.
Comparison of the present invention with the present Algorithm
Two related Algorithms, such as a 'probability condition Preference Network based Interactive Genetic Algorithm (PCPN-IGA)' and an 'Interactive Genetic Algorithm for Selecting and evolving Individuals based on an Elite Set' (Interactive Genetic Algorithms with Selecting and evolving industries Using Elite Set, IGA-SES), are used as comparison Algorithms to verify the effectiveness of the method in the aspects of searching efficiency, optimizing quality, reducing user fatigue and the like.
The experimental results of the three methods are shown in fig. 4. From fig. 4, it can be seen that the present invention is clearly superior to the comparison algorithm, which is embodied as:
a. in the aspect of searching time, the method consumes the least time, and improves the searching efficiency. The invention adopts a set evolution strategy and an NSGA-II algorithm engine, so that the population distribution is more uniform, the diversity is better, and the algorithm searching efficiency is improved. The convergence of the algorithm is further enhanced by adopting the self-adaptive intersection and mutation operation. Although it takes longer to set the evolving individuals, the overall search time is still shorter than for the comparative algorithm.
b. In the aspect of evolution algebra, the invention has the least evolution algebra, which not only improves the search efficiency, but also reduces the fatigue of users. The method adopts the individual similarity based on the user browsing behavior to estimate the individual adaptive value, and adopts the gray support vector regression machine to further predict the adaptive value, so that the adaptive value precision is improved, the evolution direction is more in line with the preference of people, and the algorithm convergence is accelerated.
The table below shows the search result mean and the number of completely matched users statistics for the three methods. The data in the table were subjected to a Mann-Whitney U test with a significance level of 0.05. As can be seen from the table, the number of the invention is the least in the two indexes of evaluating the number of individuals and searching the number of individuals, and the difference with the comparison method is obvious. The number of the present invention is the most significant, although not significantly different from the comparative method, in the best solution. This shows that the present invention can obtain the most satisfactory solutions with relatively least number of evaluations (including evolution algebra). This reflects the most efficient optimization of the present invention. The small number of search individuals is caused by the small number of evolutionary algebras. Because the comparison method is also large-scale population evolution with the same population scale, the more evolution generations, the more search individuals, so the more search individuals of the comparison method can not indicate that the search capability is better than the invention. The number of the users obtaining the complete matching solution is 3, the number of IGA-SES is 2, and the number of PCPN-IGA is only 1. This means that most users end up obtaining an approximate solution to the complex implicit performance indicator optimization problem of color matching. But the number of users of the invention which can obtain a complete matching solution is the largest, which reflects the best optimization performance of the invention. From the above analysis, it can be seen that the search performance of the present invention is the best.
a. IGA-SES-PCPN-IGA b. inventive IGA-SES-PCPN-IGA
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent variations and modifications made to the above embodiment according to the technical spirit of the present invention still fall within the scope of the technical solution of the present invention.
Claims (2)
1. An interactive set evolution calculation method based on a prediction adaptive value of a gray support vector regression machine is characterized by comprising the following steps of:
firstly, an evolution module is set, the module presents 12 individuals to a user, the individuals are color blocks, an adaptive value input text box is set below each individual, meanwhile, the evaluation time of the user to each individual is recorded through a sliding block below each individual and is used for calculating the uncertainty of the adaptive value, a target color block is set on the periphery of each individual for comparison, and the distance value between the target color block and the current individual is displayed; secondly, an evolution parameter and information statistics module is set, a user can input RGB color values by dragging a scroll bar before evolution, a target color is set, and in addition, the module also displays statistical information of evolution starting time, current evolution algebra, user evaluation individual number and total evaluation time; setting a command button module, clicking a 'start' button by a user, and initializing the system and generating an initial population; after the user evaluates the individual, clicking a 'next' button, carrying out evolution operations such as clustering, adaptive value estimation, set evolution and the like on the background by the system to generate a next generation of evolved population, circulating the process until a termination condition is met, obtaining the color individual matched with the target color by the user, and clicking an 'end' button to terminate the evolution, wherein the specific steps are as follows:
adopting a large-scale population expansion search space, and carrying out population clustering by taking a limited number of user evaluation individuals as a clustering center; then, estimating an adaptive value of a non-cluster-center individual in a cluster according to the individual similarity of the integrated individual browsing behaviors, and further predicting the individual adaptive value by adopting a gray support vector regression model to form a set evolution individual; finally, a set evolution strategy and self-adaptive cross variation operation are adopted to realize set evolution;
(1) gray support vector regression adaptive value prediction
Newly input individual adaptation value f1'(xnew(t)) the gray support vector regression machine predicted value is restored to the original sequence scale:
in the formula (I), the compound is shown in the specification,
is to the newly input individual adaptation value f1'(xnew(t)) the gray support vector regression machine prediction, f0(xk(t)) is the individual fitness value f (x)k(t)) an original sequence;
(2) ensemble evolution strategy
The j evolution individual of the t generation evolution population x (t) is recorded as
Wherein x isj(t) as a central individual, and an individual fitness value of
For evolving an individual xj(t) number of individuals involved;
measure of diversity:
where Nc is the number of individual subclasses,
wherein x isir,r＝12, …, g, are r attributes that make up an individual,
is xirThe value of the attribute of (a) is,
measure of distribution:
in the formula, d (x)j(t)) is xj(t) a minimum crowding distance of,
d*average crowding distance of population evolution individuals;
uncertainty measure:
(3) adaptive set intersection and mutation probabilities
Self-adaptive cross probability:
self-adaptive mutation probability:
in the formula (I), the compound is shown in the specification,
k1,k2,k3,k4is a parameter.
2. The interactive set evolution calculation method based on the prediction adaptive value of the gray support vector regression machine as claimed in claim 1, wherein:
in the formula (I), the compound is shown in the specification,
is for the newly input individual adaptation value f1'(xnew(t)) the gray support vector regression machine prediction, f1'(xk(t)) is the individual exact numerical fitness value f (x)k(t)) 1-AGO sequence established after normalization, K (-) being the kernel function, αk,
Is the Lagrange multiplier and b is the bias term.
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| CN110162704B (en) * | 2019-05-21 | 2022-06-10 | 西安电子科技大学 | Multi-scale key user extraction method based on multi-factor genetic algorithm |
| CN110739045B (en) * | 2019-10-14 | 2023-05-30 | 郑州航空工业管理学院 | Interactive evolution optimization method for personalized recipe design |
| CN115204064B (en) * | 2022-09-19 | 2022-12-02 | 中国石油大学(华东) | Gas injection huff-puff parameter optimization method and system in shale oil exploitation |
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