Granted Patent B2

US 12,282,845 · App. 16/671,274 · Granted Apr 22, 2025

Inventors: Jason Zhi Liang (Fremont, CA); Elliot Meyerson (San Francisco, CA); Risto Miikkulainen (Stanford, CA)

Assignee: Cognizant Technology Solutions US Corp.

G06N3/08G06N3/045

Abstract

Claims (18)

1. A computer-implemented method for co-evolution of hyperparameters and architecture in accordance with multiple objective optimization, the method comprising:

initializing a first population of modules and a first population of blueprints containing a combination of one or more existing species of modules in the first population of modules, wherein each module is a graph including at least one node representing a deep neural network (DNN) layer and corresponding hyperparameters, and further wherein the initialization process is performed during each generation of an evolution run in a plurality of generations of subsequent populations for each existing species of modules in each of the first population of modules and the first population of blueprints:

creating a first set of modules empty of species;

for each existing species of modules, determining Pareto front of individuals for each existing species in accordance with at least a first and second objective, wherein for each individual, an individual fitness function is determined for the first objective and an individual novelty score is determined for the second objective, further wherein determining the Pareto front for each existing species of individual includes,

sorting the individuals for each existing species in a descending order by the individual fitness function;

creating a new Pareto front with a first individual from each existing species;

and for each successive individual in an existing species, if a corresponding individual novelty score of a successive individual is greater than that of a preceding individual, adding the successive individual to the Pareto front;

removing individuals in the Pareto front of each existing species and adding them to the first set of modules empty of species to form a set of new species of modules;

replacing each existing species of modules with the new species of modules;

truncating each set of the new species of modules by preserving a predetermined fraction of modules within each set of new species of modules, wherein the modules within each set of new species of modules are ranked in accordance with a module fitness function and a module novelty score and the predetermined fraction of modules is preserved from the modules with the highest rankings;

generating new individuals from the truncated sets of new species of modules using procreation, including crossover and/or mutation;

wherein the new individuals are added to a next population of modules and a next population of blueprints containing a combination of one or more existing species of modules; and

after each generation,

assembling networks using the modules and blueprints from the generated new individuals,

ranking the assembled networks in accordance with a network fitness function and a network novelty score, and

preserving a predetermined fraction of assembled networks in accordance with the ranking, wherein the predetermined fraction of assembled networks is preserved from assembled networks with the highest rankings.

2. The method of claim 1 , further including sorting the Pareto front in a descending order based on the individual novelty score.

3. The method of claim 1 , wherein the first objective is performance of the individual relative to a benchmark.

Assignments (2)

Continuity (2)

References Cited (217)