Evolutionary search for robust solutions

• US 20070094167A1
• Filed: 09/06/2006
• Published: 04/26/2007
• Est. Priority Date: 09/12/2005
• Status: Active Grant

• Current Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Original Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Inventors: Sendhoff, Bernhard, Beyer, Hans-Georg
• Granted Patent: US 7,783,583 B2
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

One embodiment of the present invention is a method for optimizing a parameter set comprising object parameters, the method comprising the steps of: (a) creating an initial population of a plurality of individual parameter sets, the parameter sets comprising object parameters describing a model, structure, shape, design or process to be optimized and setting the initial population as a current parent population; (b) for each individual parameter set in a parent population mutating the parameters and optionally recombining the parameters to create an offspring population of individual parameter sets, wherein the strength of an individual object parameter mutation is enlarged by a noise contribution to enhance the robustness of the optimization; (c) evaluating a quality of each individual in the offspring population; (d) selecting individuals of the offspring population to be the next parent generation; and (e) repeating steps (b) through (d) until a termination criterion is reached.

Claims

1. A method for optimizing a parameter set comprising object parameters, the method comprising the steps of:

(a) creating an initial population of a plurality of individual parameter sets, the parameter sets comprising object parameters describing a model, structure, shape, design or process to be optimized and setting the initial population as a current parent population;

(b) for each individual parameter set in a parent population mutating the parameters and optionally recombining the parameters to create an offspring population of individual parameter sets, wherein the strength of an individual object parameter mutation is enlarged by a noise contribution to enhance the robustness of the optimization;

(c) evaluating a quality of each individual in the offspring population;

(d) selecting individuals of the offspring population to be the next parent generation; and

(e) repeating steps (b) through (d) until a termination criterion is reached.

2. The method of claim 1 wherein said parameter set includes strategy parameters.

3. The method of claim 1, wherein the strength of the noise contribution is adapted such that the estimated population variance substantially equals a prescribed variance governed by the robustness criterion.

4. The method of claim 1, wherein the noise contribution varies for the different object parameters of a given parameter set.

5. The method of claim 1 wherein the selection in step (c) is a deterministic (μ

,λ

) or (μ

+λ

) selection of evolution strategies.

6. The method of claim 1 wherein the center of mass recombination known in evolution strategies as (μ

/μ

,λ

) is used in step (b).

7. The method of claim 1 wherein the random source for mutation is normally distributed.

8. The method of claim 1, wherein the estimation of the population variance is subject to exponential smoothing.

9. The method of claim 1 for optimizing of the shape of aerodynamic or hydrodynamic structures.

10. A computer software program product executing a method according to claim 1 when running on a computing device.

11. An apparatus programmed for executing a method according to claim 1.

12. A method for optimizing a parameter set comprising object parameters, the method comprising the steps of:

(a) creating an initial population of a plurality of individual parameter sets, the parameter sets comprising object parameters describing a model, structure, shape, design or process to be optimized and possibly strategy parameters and setting the initial population as the current parent population;

(b) for each individual parameter set in the parent population, mutating the parameters and optionally recombining them, to create an offspring population of individual parameter sets;

(c) evaluating the quality of each individual in the offspring population;

(d) selecting individuals of the offspring population to be the next parent generation; and

(e) repeating steps (b) through (d) until a termination criterion is reached;

wherein the size of the population of parameter sets is varied, preferably increased depending on an average change in the evaluated quality with respect to a preceding population of parameter sets.

13. The method of claim 12, wherein said parameter set includes strategy parameters.

14. The method of claim 12, wherein the average change in quality is subject to exponential smoothing before deciding on a variation of the population size.

15. The method of claim 12, wherein the selection in step (c) is a deterministic (μ

,λ

) or (μ

+λ

) selection of evolution strategies.

16. The method of claim 12, wherein the center of mass recombination known in evolution strategies as (μ

/μ

,λ

) is used in step (b).

17. The method of claim 12 for optimizing of the shape of aerodynamic or hydrodynamic structures.

18. A computer software program product executing a method according to claim 12 when running on a computing device.

19. An apparatus programmed for executing a method according to claim 12.