Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Genetic programming II (videotape): the next generation
Genetic programming II (videotape): the next generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
The Ant Colony Metaphor for Searching Continuous Design Spaces
Selected Papers from AISB Workshop on Evolutionary Computing
A numerical approach to genetic programming for system identification
Evolutionary Computation
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Evolutionary computation in civil engineering: research frontiers
Civil and structural engineering computing: 2001
Soft computing in engineering design - A review
Advanced Engineering Informatics
Advanced Engineering Informatics
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Evolutionary and Adaptive strategies (ES & AS) for diverse multilevel search across a preliminary, whole-system design hierarchy defined by discrete and continuous variable parameters are described. Such strategies provide high-level decision support when integrated with preliminary design software describing the major elements of an engineering system. Initial work involving a Structured Genetic Algorithm (stGA) with appropriate mutation regimes to encourage search diversity is described and preliminary results are presented. The shortcomings of the stGA approach are identified and alternative strategies are introduced. A dual agent strategy (GAANT) involving elements of an ant colony search and an evolutionary search concurrently manipulating the discrete and continuous variable parameter sets is presented. Appropriate communication between the two search agents results in a more efficient search across the hierarchy than that achieved by the stGA, while also simplifying the chromosomal representation. This simplification allows the further development of the preliminary design hierarchy in terms of complexity. The technique therefore represents a significant contribution to configuration design where multilevel, mixed discrete/continuous parameter design problems can be prevalent.