Supporting Conceptual Design Innovation through Interactive

Evolutionary Systems

I.C. ParmeeAdvanced Computation in Design and Decision-making

CEMS, University of the West of England Bristol, BS16 4QYEmail: ian.parmee@uwe.ac.uk

A far more detailed description of aspects of this work can be found in the following references:

Parmee I. C., 2002, Improving Problem Definition through Interactive Evolutionary Computation. Journal of Artificial Intelligence in Engineering Design, Analysis and Manufacture,16 (3), Cambridge Press.Parmee I. C., Cvetkovic C., Watson A. H., Bonham C. R., 2000, Multi-objective Satisfaction within an Interactive Evolutionary Design Environment. Journal of Evolutionary Computation. 8 (2), MIT Press, pp 197 – 222.Cvetkovic D., Parmee I. C., 2001, Preferences and their Application in Evolutionary Multiobjective Optimisation. IEEE Transactions on Evolutionary Computation, 6(1), pp 42 - 57.Cvetkovic D., Parmee I. C., Agent-based Support Within an Interactive Evolutionary Design System. Artificial Intelligence for Engineering Design, Analysis and Manufacturing Journal; Cambridge University Press , Vol.16 No.5, (Nov 2002 - in press). Parmee I. C. Evolutionary and Adaptive Computing in Engineering Design. Springer Verlag, London, (2001).

Interactive evolutionary design strategies support:

• the extraction of optimal design information;

• its presentation to the designer;

• subsequent human-based modification of the problem domain based upon knowledge gained from the information received.

Iterative designer / evolutionary search processes result in:

• a better understanding of the problem;

• improved machine-based representation of the design domain;

And through continuous user interaction strongly support new concept formulation, innovation, discovery and creativity.

Prototype Interactive Evolutionary Design System (IEDS)

Scenario (A)Evolution

Scenario (C)Evolution

Scenario (B)Evolution

Machine-Based Agents

Rule-BasedPreferences

External Agents (Design Team)

On-line Database

Informationgatheringprocesses

Information Gathering via Cluster-oriented Genetic Algorithms

How?

• Highly explorative GA / GAs

• Solutions extracted and passed through Adaptive Filter

• Better solutions pass into Final Clustering Set - defines HP regions

Application of COGA to Preliminary Airframe Design

1 2

3 4

Figures 1 to 4 show the effect of increasing the filter threshold setting. Low settings of figure 1 result in a large cluster of medium fitness solutions increasing the filter setting results in the identification of the two disjoint clusters of figure 4.

High-performance regions relating to various objectives

Lines define boundaries of the high performance regions for each objective - shaded area defines common region containing HP solutions that satisfy more than one objective. (a) Common region containing high performance solutions for Ferry Range and Turn Rate identified but Specific Excess Power(SEP)cannot be satisfied.; (b)Relaxing filter threshold for SEP allows lower fitness SEP solutions through, boundary moves towards feasible region; (c) Further relaxation results in the identification of a feasible region for all objectives.

a b c

• Simple linguistic rules facilitate direct preference manipulation by the designer e.g:

relation intended meaning is equally important < is less important << is much less important > is more important >> is much more important

• Ranked preferences relating to multi-objectives can be introduced and altered during an evolutionary run.

• Designer only required to answer a minimal set of straightforward questions

• Preferences transformed into numerical objective weightings

Objective Preferences

Co-evolutionary Multi-objective Satisfaction

•Concurrent GA processes each optimise one objective

• Fitness measure for individuals within each GA is adjusted by comparing distance between solutions of one objective with those of others

• Penalty relating to the degree of diversity of variables of each objective process is imposed

• Initial convergence upon individual objectives leads to overall convergence of all processes upon a single compromise design region.

(a) Ferry Range is much more important(b) All objectives are of equal importance(c) Ferry Range is much less important

Agents for Scenario / Dynamical Constraint Satisfaction

• Designer likely to have several ideal scenarios such as: ‘I would like objective A to be greater than 0.6 and objective C to be less than 83.56; objectives B, D, E should be maximised; variable 2 should have a value of between 128.0 and 164.5; a value graeter than o.32 is prefered for variable 7 ’

• Incremental Agent operates as follows:

1 Use designer’s original preferences for both objectives and scenarios and run optimisation process

2 If some scenarios are not fulfilled, agent suggests increase in their importance of these scenarios

3 If some scenarios still not fulfilled even when classed as ‘most important’ agent suggests change to variable ranges in scenario.

4 If some scenarios still not fulfilled agent reports to designer and asks for assistance.

Closed preference / scenario loop