Cognizant Technology Solutions U.S. Corporation

Blackberry Corporation

Sentient Technologies (barbados) Limited

Cognizant Technology Solutions U.S. Corportion

Daniel Edward Fink

Hormoz Shahrzad

Babak Hodjat

Kaivan Kamali

Alastair Robert Gourlay

Alastair Gourlay

Risto Miikkulainen

Roger Petersen

Olivier Francon

Roger James Petersen

A system and process for generalizing an evolutionary process applied to a particular domain involving different problems includes a researcher module for generating a configuration specification applicable to a particular problem. An evolution module parses the configuration specification into a representative tree structure, assembles policies for each node in the tree structure, and generates candidate genomes using the policies for each node in the tree structure. The policies may be applied to new data or data from prior runs to generate candidate genomes. The evolution module translates internal representations of the generated candidate genomes into known representations of the candidate genome for evaluation in accordance with the particular domain parameters by a candidate evaluation module.

Systems and methods for providing data-driven evolution of arbitrary data structures

A composite novelty method approach to deceptive problems where a secondary objective is available to diversify the search is described. In such cases, composite objectives focus the search on the most useful tradeoffs and allow escaping deceptive areas. Novelty-based selection increases exploration in the focus area, leading to better solutions, faster and more consistently and it can be combined with other fitness-based methods.

Enhanced optimization with composite objectives and novelty-diversity selection

Described herein is a process which facilitates segmented security between domain-specific data sets being evaluated as part of a candidate evaluation service and third-party evolution services, wherein the data sets are not transmitted to the evolution service which is evolving candidates for evaluation. This enables customers with secure data sets to use candidate evolution services securely by obtaining a population of potentially optimal candidate models to evaluate and then optimizing on those data sets in their own secure fashion.

Systems and methods for providing secure evolution as a service

Roughly described, a computer-implemented evolutionary data mining system includes a memory storing a candidate gene database in which each candidate individual has a respective fitness estimate; a gene pool processor which tests individuals from the candidate gene pool on training data and updates the fitness estimate associated with the individuals in dependence upon the tests; and a gene harvesting module for deploying selected individuals from the gene pool, wherein the gene pool processor includes a competition module which selects individuals for discarding in dependence upon their updated fitness estimate. The system maintains the ancestry count for each of the candidate individuals, and may use this information to adjust the competition among the individuals, to adjust the selection of individuals for further procreation, and/or for other purposes.

Data mining technique with maintenance of ancestry counts

Roughly described, a training database contains N segments of data samples. Candidate individuals identify a testing experience level, a fitness estimate, a rule set, and a testing set TSof the data samples on which it is tested. The testing sets have fewer than all of the data segments and they are not all the same. Testing involves testing on only the individual's assigned set of data segments, updating the fitness estimates and testing experience levels, and discarding candidates through competition. If an individual reaches a predetermined maturity level of testing experience, then validating involves further testing it on samples of the testing data from a testing data segment other than those in the individual's testing set TS. Those individuals that satisfy validation criteria are considered for deployment.

Data mining technique with n-Pool evolution

Roughly described, individuals in both a training system and in a production system include a label field in their rule outputs. Positions entered by an individual are maintained in a status record for the individual, including the label output by the rule which triggered entry of that position. Rules that assert exiting or partial exiting of a position also output the label from the rule which triggered the assertion, and are effective only so far as matching positions exist or remain in the individual's status record, including a matching label. Labels present in the status record also can be referenced in conditions of a rule. During evolution, a rule's output label is subject to crossover and/or mutation just like the conditions and output assertions.

Data mining technique with position labeling

When a user enters, initializes, or otherwise starts using a navigation function, such as a navigation function on a mobile phone or a stand-alone device, a destination is automatically selected for route generation and production of a navigation output (e.g., an Estimated Time of Arrival (ETA) at the destination). The destination can be selected based on current proximity to a location having an associated pre-defined destination. Such pre-defined destination can be associated with the location by the user. A time of day criteria can also be imposed. In one example, locations proximate a work place can be associated with home as a pre-defined destination, and vice versa. A time of day criteria can be imposed, such that even if proximate work and it is in the morning, a route and ETA to home will not be generated.

System and method of automatic destination selection

Evolutionary technique with n-pool evolution

Aspects provide for a navigation function implemented on a device that has a communication capability (e.g., a mobile phone) in which the navigation function automatically sends an Estimated Time of Arrival (ETA) to a contact associated with a destination selected for navigation purposes. For example, when a user activates a navigation function on his mobile phone, and selects a destination for which a route will be generated from his current location to the destination, a contact phone number associated with that destination will be sent a Short Message System (SMS) message with the calculated ETA. Provisions can be made for automatic updates as the route is traveled. Other information pertaining to the reasons for the ETA can be selected or automatically generated by the navigation function.

System and method of sending an arrival time estimate

A route comprises interconnected road segments that can be traveled to get from a location to a destination. Previously, routes are represented by display of the spatial arrangement of such road segments (e.g., a map). Here, a route is depicted as a linear shape, and portions of the linear shape represent portions of the route, regardless of their spatial arrangement. A scale is applied between a characteristic of the route and the linear shape. Information elements can be depicted at points on the linear shape that correspond, based on the scale, to locations on the route where such information applies. Portions of the linear shape can be colored or cross-hatched according to traffic congestion conditions. Incident reports can be represented by indicators along the linear shape. Alternate routes (detours) can be represented by respective separate linear shapes; lead lines can connect such linear shapes to points of the route linear shape where each such detour would be taken.

Growing community of inventors

Berkeley, CA, United States of America

Daniel Edward Fink