Applications of Genetic Algorithms

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2.10. Applications of Genetic Algorithms

Unlike most methods of combinatorial optimisation, GAs did not initially have an underlying mathematical model. As such, they spent some time demonstrating themselves on a number of famous mathematical problems such as the travelling salesperson problem and the k-armed bandit problem before tackling more practical issues Davis, 1991. By 1989 when David E Goldberg released the seminal “Genetic algorithms in search, optimisation and machine learning”, the field had begun the brightest phase of its career- that of Being Applicable to Real World Problems Davis, 1991. Any problem which can be phrased so as to require the minimising or maximising of some function can be addressed by GAs Davis, 1991. In particular, where this function is dependent upon a great many variables, such that more conventional methods are out of their depth, evolutionary methods become attractive Corne and Ross, 1995. Particularly noteworthy applications of GA include the solving of pipe network optimisation problems Anderson and Simpson, 1996 transportation problems Gen and Cheng, 1997 conformational analysis of DNA Davis, 1991 image processing and machine learning Buckles and Petry, 1992 and, of course, scheduling problems Burke and Ross 1996; Buckles and Petry, 1992. GAs are by their very nature, easily translated to parallel systems Davis, 1991. Each creature is to some part separate from each other 44 creature and related, to some degree. At the moment of breeding and death, there must be some interaction between one creature and the colony or some portion of the colony. Tournament selection is a method of choosing for extinction or for selecting for breeding which is most effectively executed on a parallel system. In this case, it is not necessary for any one machine to know the average fitness of the entire population, only for the machines possessing the combatants to briefly communicate. The application of GAs to parallel architectures has seen a large improvement in their performance, and has created a large amount of interest Davis, 1991; Buckles and Petry, 1997. This appears to be the major direction in which GA is heading. GAs are advancing by containing less of a close metaphor with natural evolution instead conforming only to that essence of evolution, which allows it to work. For example, data structures are replacing binary numbers as the most common form of representing genetic material. In modern GAs, chromosomes are rarely fully encoded Davis, 1991. 2.11. The Concept of Programming Language 2.11.1. PHP