document: Global optimisation algorithms are used in this study to solve the optimisation problem as they are known to be efficient in incorporating statistical information and dealing with complicated objective functions that have multiple local minima/maxima. The genetic algorithm (GA) is such a global optimisation technique that mimics biological evolution processes and is used in this particular study. The algorithm starts with a random selection of a population from the decision variable domain (X). The genetic algorithm repeatedly modifies this population. At each step, the algorithm selects a group of individual values from the population (parent) which are evolved through crossover or mutation to produce members of the next generation. This process is repeated for several generations until an optimum solution is reached. See [19] for a fuller description of the GA.
extractive_keyphrases: crossover decision variable domain ga genetic algorithm global optimisation algorithms global optimisation technique incorporating statistical information mimics biological evolution processes mutation objective functions parent population selects a group of individual values solve the optimisation problem x
abstractive_keyphrases: local minima/maxima random selection of a population