Adaptive Gene Expression Programming Using a Simple Feedback Heuristic

Gene expression programming has been shown to be an important algorithm in the optimisation of complex systems. However, it has many more operators and parameters than standard genetic programming, each of which needs to be set when applying the algorithm to new problems. In this paper, an adaptive approach for setting probabilities for genetic operators in gene expression programming (GEP) using parameter control is investigated. The adaptive approach implements simple functions that regulate the probabilities of using a genetic operator e.g. mutation, in a given generation based on the comparison between the fitness of the parent organism and child organism in the previous generations. Using this method, it is shown that by using an adaptive approach an increase in the success rate of a run and decrease in the number of generations required in order to achieve success can be achieved.