Improving problem definition through interactive evolutionary computation
TL;DR: In this paper, the authors focus on background human-computer interaction relating to the machine-based generation of high-quality design information that, when presented in an appropriate manner to the designer, supports a better understanding of a problem domain.
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Abstract: Poor definition and uncertainty are primary characteristics of conceptual design processes. During the initial stages of these generally human-centric activities, little knowledge pertaining to the problem at hand may be available. The degree of problem definition will depend on information available in terms of appropriate variables, constraints, and both quantitative and qualitative objectives. Typically, the problem space develops with information gained in a dynamical process in which design optimization plays a secondary role, following the establishment of a sufficiently well-defined problem domain. This paper concentrates on background human–computer interaction relating to the machine-based generation of high-quality design information that, when presented in an appropriate manner to the designer, supports a better understanding of a problem domain. Knowledge gained from such information combined with the experiential knowledge of the designer can result in a reformulation of the problem, providing increased definition and greater confidence in the machine-based representation. Conceptual design domains related to gas turbine blade cooling systems and a preliminary air frame configuration are introduced. These are utilized to illustrate the integration of interactive evolutionary strategies that support the extraction of optimal design information, its presentation to the designer, and subsequent human-based modification of the design domain based on knowledge gained from the information received. An experimental iterative designer or evolutionary search process resulting in a better understanding of the problem and improved machine-based representation of the design domain is thus established.
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Figures
Fig. 2. A schematic of the interactive evolutionary design station. 
Fig. 3. The application of variable mutation COGA to preliminary airframe design. 
Fig. 5. The identification of compromise HP regions relating through filter threshold relaxation.~a! A common region for ferry range and attained turn rate~ATR! has been identified but specific excess power~SEP! objectives cannot be satisfied.~b! Relaxing the SEP filter threshold allows lower fitness solutions through and boundary moves.~c! Further relaxation results in the identification of a common region for all objectives. 
Fig. 8. The ~a! ferry range is much more important.~b! All objectives are of equal importance.~c! The ferry range is much less important. 
Fig. 7. The graphs show the convergence of the four evolving objectives on a best compromise region of the design space. Each graph provides data relating to a particular objective. Objectives are coded as shown in the key. 
Fig. 6. Establishing equivalence classes.
Citations
Compositional pattern producing networks: A novel abstraction of development
TL;DR: Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed.
An interactive evolutionary multi-objective optimization and decision making procedure
Shamik Chaudhuri,Kalyanmoy Deb +1 more
- 01 Mar 2010
TL;DR: An interactive EMO procedure which will involve a decision-maker in the evolutionary optimization process and help choose a single solution at the end is suggested and would hopefully encourage both practitioners and researchers to pay more attention in viewing the multi-objective optimization as a aggregate task of optimization and decision-making.
93
A Case-Based Micro Interactive Genetic Algorithm (CBMIGA) for interactive learning and search: Methodology and application to groundwater monitoring design
TL;DR: A new interactive optimization algorithm is proposed - Case-Based Micro Interactive Genetic Algorithm - that uses a case-based memory and case- based reasoning to manage the effects of nonstationarity in decision maker's preferences within the search process without impairing the performance of the search algorithm.
42
Supporting implicit learning via the visualisation of COGA multi-objective data
I.C. Parmee,J.A.R. Abraham +1 more
- 19 Jun 2004
TL;DR: The paper speculates upon the development of human-centric evolutionary conceptual design systems that support implicit learning through the succinct visual presentation of data relating to both variable and objective space that could support intuitional understanding of the problem domain.
31
A cross-disciplinary technology transfer for search-based evolutionary computing: from engineering design to software engineering design
TL;DR: The mass of software design solution variants produced suggests that transferring search-based technology across disciplines has significant potential to provide computationally intelligent tool support for the conceptual software designer.
26
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Preferences and their application in evolutionary multiobjective optimization
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TL;DR: A new preference method is described and its usefulness was demonstrated in a real-world project of conceptual airframe design and theoretical results relating to complexity and sensitivity of the algorithm are presented and discussed.
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Multiobjective Satisfaction within an Interactive Evolutionary Design Environment
TL;DR: The development of certain elements within an interactive evolutionary conceptual design environment that allows off-line processing of such information leading to a redefinition of the design space are described.
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