Decision Support Systems - Special double issue: unified programming
Gado: a genetic algorithm for continuous design optimization
Gado: a genetic algorithm for continuous design optimization
Constrained multi-objective optimization using steady state genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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GA's have more success with optimizing a single configuration than with optimizing multiple configurations with connectivity constraints tying them together, i.e., variable geometry problems such as designing the variable geometry wings of a plane. This paper describes a GA based approach to solve variable geometry optimization problems where (a) the connectivity requirement cannot be easily specified or tested, (b) the space of configurations is made up of multiple disconnected spaces, thus making it likely that a GA would find sets of configurations that are not connected and (c) the cost of testing for connectivity, while examining each pair of configurations, is prohibitive. The approach has been tested and evaluated on a problem from computational biology, modeling the bones of a snake jaw.