Abstract
The usefulness of the genetic algorithm (GA) as judged by numerous applications in engineering and other contexts cannot be questioned. However, to make the application successful, often considerable effort is needed to customise the GA to suit the problem or class of problems under consideration. Perhaps the most basic decision which the designer of a GA makes, is whether to use binary or real coding. If the variable of the parameter space of an optimisation problem is continuous, a real coded GA is possibly indicated. Real numbers have a floating-point representation on a computer and the decision space is always discretised; it is not immediately evident that real coding should be the preferred method for encoding this particular problem. We re-visit this, and other decisions, which GA designers need to make. We present simulations on a standard test function, which show the result that no one GA performs best on every test problem. Perhaps the initial choice to code a problem using a real or binary coding is a false dichotomy. What counts are the algorithms for implementing the genetic operators and these algorithms are a consequence of the coding. References D. Rani and M. M. Moreira. Simulation--optimization modeling: A survey and potential application in reservoir systems operation. Water Resources Management. Springer Netherlands, 2009. http://www.springerlink.com/content/p61p535r2277r852/ John H. Holland. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence. Ann Arbor: University Michigan Press, 1975. http://mitpress.mit.edu/catalog/author/default.asp?aid=3235 Z. Michalewicz. Genetic algorithms + data structures = evolution programs. 3rd edition, New York: Springer--Verlag. http://www.springer.com/computer/ai/book/978-3-540-60676-5 D. Fogel. Evolutionary computation: toward a new philosophy of machine intelligence. 3rd edition, IEEE Press, 2006. http://ebooks.ebookmall.com/ebook/232338-ebook.htm C. Macnish. Towards unbiased benchmarking of evolutionary and hybrid algorithms for real-valued optimisation. Connection Science, 19:4, 2007, 361--385. http://www.springerlink.com/content/2446104674981574/
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