Evolutionary search for robust solutions

• US 7,783,583 B2
• Filed: 09/06/2006
• Issued: 08/24/2010
• 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: Beyer, Hans-Georg, Sendhoff, Bernhard
• Primary Examiner(s): Sparks; Donald
• Assistant Examiner(s): COUGHLAN, PETER D
• Application Number:
  US11/517,135
• Publication Number:
  US 20070094167A1
• Time in Patent Office: 1,448 Days
• Field of Search: None
• 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 computer based method for optimizing a design of a hydrodynamic or aerodynamic structure, 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 the design to be optimized and setting the initial population as a current parent population;

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

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

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

(e) repeating steps (b) through (d) until a termination criterion is reached to optimize the design.

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 a 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. A computer including a software program, which when executed by a computing device performs a method according to claim 1.

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

11. A computer for optimizing a design of a hydrodynamic or aerodynamic structure, comprising:

parameter sets means for creating an initial population of a plurality of individual parameter sets,

the parameter sets comprising object parameters describing the design to be optimized and possibly strategy parameters and setting the initial population as the current parent population;

mutating means, 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, wherein a strength of an individual object parameter mutation is enlarged by a noise contribution, wherein the noise contribution is added to said individual object parameter value whereby the noise contribution is a mutation term, to enhance the robustness of the optimization;

evaluating means for evaluating a quality of each individual in the offspring population;

selecting means for selecting individuals of the offspring population to be the next parent generation based upon said quality; and

terminating means for repeatedly implementing the mutating means, evaluating means and selecting means until a termination criterion is reached for optimizing the design;

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.

12. The system of claim 11, wherein said parameter set includes strategy parameters.

13. The system of claim 11, wherein the average change in quality is subject to exponential smoothing before deciding on a variation of the population size.

14. The system of claim 11, wherein the selection in step (c) is a deterministic (μ

,λ

) or (μ

+λ

) selection of evolution strategies.

15. The system of claim 11, wherein the center of mass recombination known in evolution strategies as (μ

/μ

,λ

) is used in step (b).