Implicit alternative splicing for genetic algorithms

In this paper we present a new nature-inspired variation operator for binary encodings in genetic algorithms (GAs). Our method, called implicit alternative splicing (iAS), is repeatedly applied to the individual encodings in the algorithm´s population and inverts randomly chosen segments of decreasing size in a systematic fashion. Its goal is to determine the largest possible segment the inversion of which yields no loss in the encoding´s quality. The application of iAS potentially produces a new encoding of equal or greater quality that is maximum possible Hamming distance from its source. This allows iAS to uphold the diversity of the population even if a minimal population size is chosen. This significantly improves the performance of an otherwise standard GA on a representative set of three different optimisation problems. Empirical results are compared and analysed and future work prospects are considered.