Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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Complexity Engineering deals with harnessing the power of Cellular Automata (CA) like simple models to solve real life difficult and complex engineering problems, dealing with systems that have very simple components that collectively exhibit complex behaviors. Cellular Automata (CA) are examples of dynamical systems which may instead exhibit "self organizing" behavior with increasing time. CAs are commonly used in modeling modular systems. An important aspect of modularity in engineering systems is the abstraction it makes possible. Once the construction of a particular module has been completed, the module can be treated as a single object, and only its behavior need be considered, wherever the module appears. One such application of modularity is described in this paper where a structural plate is considered as composed of smaller "structural modules" which are considered as cells in a lattice of sites in a CA and have discrete values updated in discrete time steps according to local rules. These local rules are generally fixed in a CA, but we consider these rules as evolvable. To evolve the local rules, we use the Genetic Algorithm (GA) model. Though the application described here is simple, it will still serve to demonstrate that the GA can discover CA rules that give rise to emergent computational strategies by self-organization, to exhibit globally coordinated tasks in optimization by simple local interactions only.