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Abstract

This paper describes a comprehensive framework for generative evolutionary design. The key problem that is identified is generating alternative designs with an appropriate level of variability. Within the proposed framework, the design process is split into two phases: in the first phase, the design team develops and encodes the essential and identifiable character of the designs to be generated and evolved; in the second phase, the design team uses an evolutionary system to generate and evolve designs that embody this character. This approach allows design variability to be carefully controlled. In order to verify the feasibility of the proposed framework, a generative process capable of generating controlled variability is implemented and demonstrated.

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... This paper describes a design method that supports a generative evolutionary approach that would allow a design team to evolve challenging designs (Janssen 2004;Janssen et al. 2006;Janssen 2006). The key concept is the notion of a design entity that captures a set of design ideas by one design team. ...
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I Introduction.- Evolutionary Algorithms - An Overview.- Robust Encodings in Genetic Algorithms.- II Architecture and Civil Engineering.- Genetic Engineering and Design Problems.- The Generation of Form Using an Evolutionary Approach.- Evolutionary Optimization of Composite Structures.- Flaw Detection and Configuration with Genetic Algorithms.- A Genetic Algorithm Approach for River Management.- Hazards in Genetic Design Methodologies.- III Computer Science and Engineering.- The Identification and Characterization of Workload Classes.- Lossless and Lossy Data Compression.- Database Design with Genetic Algorithms.- Designing Multiprocessor Scheduling Algorithms Using a Distributed Genetic Algorithm System.- Prototype Based Supervised Concept Learning Using Genetic Algorithms.- Prototyping Intelligent Vehicle Modules Using Evolutionary Algorithms.- Gate-Level Evolvable Hardware: Empirical Study and Application.- Physical Design of VLSI Circuits and the Application of Genetic Algorithms.- Statistical Generalization of Performance-Related Heuristics for Knowledge-Lean Applications.- IV Electrical, Control and Signal Processing.- Optimal Scheduling of Thermal Power Generation Using Evolutionary Algorithms.- Genetic Algorithms and Genetic Programming for Control.- Global Structure Evolution and Local Parameter Learning for Control System Model Reductions.- Adaptive Recursive Filtering Using Evolutionary Algorithms.- Numerical Techniques for Efficient Sonar Bearing and Range Searching in the Near Field Using Genetic Algorithms.- Signal Design for Radar Imaging in Radar Astronomy: Genetic Optimization.- Evolutionary Algorithms in Target Acquisition and Sensor Fusion.- V Mechanical and Industrial Engineering.- Strategies for the Integration of Evolutionary/Adaptive Search with the Engineering Design Process.- Identification of Mechanical Inclusions.- GeneAS: A Robust Optimal Design Technique for Mechanical Component Design.- Genetic Algorithms for Optimal Cutting.- Practical Issues and Recent Advances in Job- and Open-Shop Scheduling.- The Key Steps to Achieve Mass Customization.
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