Exploration and innovation in design: towards a computational model
Exploration and innovation in design: towards a computational model
Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Evolutionary and Adaptive Computing in Engineering Design: The Integration of Adaptive Search Exploration and Optimization with Engineering Design Pro
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Stalk: An Interactive System for Virtual Molecular Docking
IEEE Computational Science & Engineering
Design, Analogy, and Creativity
IEEE Expert: Intelligent Systems and Their Applications
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Selected Papers from AISB Workshop on Evolutionary Computing
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Multiobjective Satisfaction within an Interactive Evolutionary Design Environment
Evolutionary Computation
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
Compositional pattern producing networks: A novel abstraction of development
Genetic Programming and Evolvable Machines
Supporting free-form design using a component based representation: an overview
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Computational steering of a multi-objective evolutionary algorithm for engineering design
Engineering Applications of Artificial Intelligence
Soft computing in engineering design - A review
Advanced Engineering Informatics
International Journal of Computer Applications in Technology
Evolutionary design search, exploration and optimisation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Agent-based support for interactive search in conceptual software engineering design
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Using XCS to describe continuous-valued problem spaces
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
An internet-scale idea generation system
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special section on internet-scale human problem solving and regular papers
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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.