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Various meta-heuristics were trialled for solving the cable routing problem (8). A genetic algorithm was finally selected as, in initial experimentation, it was found to be the most robust. Furthermore genetic algorithms are the most popular approach used in operational research problems and therefore the most developed. Genetic algorithms (GAs) are search meta-heuristics which simulate the process of evolution. As such, much of the terminology associated with this approach is borrowed from genetics. Possible solutions are encoded as numeric vectors termed ‘chromosomes’. An initial population of BMS-927711 is generated, either randomly or using a pseudo-random heuristic. This population is evaluated using a fitness function and the fittest chromosomes are selected and mutated to populate the next generation. The pseudo-random nature of the mutation ensures that the search space is effectively explored, while a good chromosome representation minimises this search space through eliminating repeated solutions and thereby reducing redundancy . GAs are used on a wide variety of problems and are adapted to purpose by designing suitable chromosomes and mutations. The particular GA being developed for the application considered in this work shall be referred to as TidalArrayRouting (TAR) to distinguish design choices specific to this algorithm for this application from GAs in general.