Patent Issued for Differential evolution algorithm to allocate resources (USPTO 11521267): Hartford Fire Insurance Company
2022 DEC 27 (NewsRx) -- By a
The assignee for this patent, patent number 11521267, is
Reporters obtained the following quote from the background information supplied by the inventors: “An enterprise may want to analyze a set of resource types. For example, an insurer might want to analyze a portfolio of assets, such as stocks, bonds, hedge fund assets, etc. In particular, the enterprise might want to optimize an allocation of resources to improve a particular result (e.g., improve net investment income) while satisfying or more constraints (e.g., a portfolio duration). When the constraints are linear, a mean-variance optimization with a quadradic algorithm is typically performed to achieve such a result. In some cases, however, one or more constraints may be non-linear (e.g., a book yield), in which case a quadradic algorithm cannot be used.
“It would be desirable to provide improved systems and methods to accurately and/or automatically analyze resource allocations. Moreover, the results should be easy to access, understand, interpret, update, etc.”
In addition to obtaining background information on this patent, NewsRx editors also obtained the inventors’ summary information for this patent: “According to some embodiments, systems, methods, apparatus, computer program code and means are provided to accurately and/or automatically analyze resource allocations in a way that provides fast and useful results and that allows for flexibility and effectiveness when responding to those results.
“Some embodiments are directed to a resource allocation analysis system implemented via a back-end application computer server. A resource data store may contain electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter. The back-end application computer server may receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters. The computer server may then execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint and generate resource analysis results. The back-end application computer server may, according to some embodiments, perform a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching.
“Some embodiments comprise: means for receiving, by the back-end application computer server from a resource data store, information about a set of resource types to be analyzed, including associated resource parameters, wherein the resource data store contains electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter; and means for executing a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint and generate resource analysis results. The back-end application computer server performs a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching.
“In some embodiments, a communication device associated with a back-end application computer server exchanges information with remote devices in connection with an interactive graphical user interface. The information may be exchanged, for example, via public and/or proprietary communication networks.
“A technical effect of some embodiments of the invention is an improved and computerized way to accurately and/or automatically evaluate resource allocations in a way that provides fast and useful results. With these and other advantages and features that will become hereinafter apparent, a more complete understanding of the nature of the invention can be obtained by referring to the following detailed description and to the drawings appended hereto.”
The claims supplied by the inventors are:
“1. A resource allocation analysis system implemented via a back-end application computer server, comprising: (a) a resource data store associated with an encrypted database management system and containing electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter; (b) the back-end application computer server, coupled to the resource data store, including: a computer processor, and a computer memory, coupled to the computer processor, storing instructions that, when executed by the computer processor cause the back-end application computer server to: (i) receive, from the resource data store, information about a set of resource types to be analyzed, including the associated resource parameters, and (ii) if a constraint type is non-linear: execute a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint associated with net investment income and generate resource analysis results, and (iii) if a constraint type is linear: execute a quadratic algorithm, instead of the differential evolutionary algorithm, to optimize the set of resource types based on the linear constraint and generate resource analysis results, thereby reducing an exchange of information associated with the back-end application computer server as compared to execution of the differential evolutionary algorithm, wherein the back-end application computer server performs a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching based on skew; and © a communication port coupled to the back-end application computer server to facilitate a transmission of data with remote user devices to support interactive user interface displays, including the resource analysis results, via at least one security feature component and a distributed communication network.
“2. The system of claim 1, wherein the resampling process comprises: constructing a risk-return curve using mean-variance optimization; executing resamples with varied return distribution or confidence intervals; and constructing a new risk-return curve by averaging the resampled results.
“3. The system of claim 1, wherein the moment matching is associated with at least one of: (i) mean, (ii) volatility, and (iii) a distribution shape.
“4. The system of claim 1, wherein the resource types comprise asset types of an insurer and the set of resource types comprises an asset portfolio.
“5. The system of claim 4, wherein the asset types include at least one of: (i) stocks, (ii) bonds, (iii) hedge fund assets, (iv) high yield corporate assets, (v) emerging market assets, (vi) tax-exempt municipal assets, (vii) private equity, (viii) governmental treasury assets, and (ix) cash.
“6. The system of claim 4, wherein the at least one non-linear constraint is associated with: (i) capital consumption, (ii) asset turnover, (iii) book yield, and (iv) realized capital gains.
“7. The system of claim 4, wherein the differential evolution algorithm further optimizes expected return.
“8. A computerized resource allocation analysis method implemented via a back-end application computer server, comprising: receiving, by the back-end application computer server from a resource data store, information about a set of resource types to be analyzed, including associated resource parameters, wherein the resource data store is associated with an encrypted database management system and contains electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter; if a constraint type is non-linear: executing a differential evolutionary algorithm to optimize the set of resource types based on the at least one non-linear constraint associated with net investment income and generate resource analysis results, wherein the back-end application computer server performs a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching based on skew; if a constraint type is linear: executing a quadratic algorithm, instead of the differential evolutionary algorithm, to optimize the set of resource types based on the linear constraint and generate resource analysis results, thereby reducing an exchange of information associated with the back-end application computer server as compared to execution of the differential evolutionary algorithm; and transmitting, via a communication port coupled to the back-end application computer server, data with remote user devices to support interactive user interface displays, including the resource analysis results, via at least one security feature component and a distributed communication network.
“9. The method of claim 8, wherein the set of resource types are optimized based on at least one non-linear constraint and the differential evolutionary algorithm is executed.
“10. The method of claim 9, wherein the resampling process comprises: constructing a risk-return curve using mean-variance optimization; executing resamples with varied return distribution or confidence intervals; and constructing a new risk-return curve by averaging the resampled results.
“11. The method of claim 9, wherein the moment matching is further associated with at least one of: (i) mean, (ii) volatility, and (iii) a distribution shape.
“12. The method of claim 9, wherein the resource types comprise asset types of an insurer and the set of resource types comprises an asset portfolio.
“13. The method of claim 12, wherein the asset types include at least one of: (i) stocks, (ii) bonds, (iii) hedge fund assets, (iv) high yield corporate assets, (v) emerging market assets, (vi) tax-exempt municipal assets, (vii) private equity, (viii) governmental treasury assets, and (ix) cash.
“14. The method of claim 12, wherein the at least one non-linear constraint is associated with: (i) capital consumption, (ii) asset turnover, (iii) book yield, and (iv) realized capital gains.
“15. The method of claim 12, wherein the differential evolution algorithm further optimizes expected return.
“16. A non-transitory, computer-readable medium storing instructions, that, when executed by a processor, cause the processor to perform a resource allocation analysis method implemented via a back-end application computer server, the method comprising: receiving, by the back-end application computer server from a resource data store, information about a set of resource types to be analyzed, including associated resource parameters, wherein the resource data store is associated with an encrypted database management system and contains electronic records associated with a set of resource types, each electronic record including an electronic record identifier and resource parameter; if a constraint type is non-linear: executing a differential evolutionary algorithm to optimize the set of resource types based on at least one non-linear constraint associated with net investment income and generate resource analysis results, wherein the back-end application computer server performs a resampling process that uses non-parameterized historical data, regression on at least one resource type, and moment matching based on skew; if a constraint type is linear: executing a quadratic algorithm, instead of the differential evolutionary algorithm, to optimize the set of resource types based on the linear constraint and generate resource analysis results, thereby reducing an exchange of information associated with the back-end application computer server as compared to execution of the differential evolutionary algorithm; and transmitting, via a communication port coupled to the back-end application computer server, data with remote user devices to support interactive user interface displays, including the resource analysis results, via at least one security feature component and a distributed communication network.
“17. The medium of claim 16, wherein the resampling process comprises: constructing a risk-return curve using mean-variance optimization; executing resamples with varied return distribution or confidence intervals; and constructing a new risk-return curve by averaging the resampled results.
“18. The medium of claim 16, wherein the moment matching is associated with at least one of: (i) mean, (ii) volatility, and (iii) a distribution shape.
“19. The medium of claim 16, wherein the resource types comprise asset types of an insurer and the set of resource types comprises an asset portfolio.
“20. The medium of claim 19, wherein the asset types include at least one of: (i) stocks, (ii) bonds, (iii) hedge fund assets, (iv) high yield corporate assets, (v) emerging market assets, (vi) tax-exempt municipal assets, (vii) private equity, (viii) governmental treasury assets, and (ix) cash.
“21. The medium of claim 19, wherein the at least one non-linear constraint is associated with: (i) capital consumption, (ii) asset turnover, (iii) book yield, and (iv) realized capital gains.
“22. The medium of claim 19, wherein the differential evolution algorithm further optimizes expected return.”
For more information, see this patent: Caputo, Edward C. Differential evolution algorithm to allocate resources.
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