Gabo: Gene Analysis Bitstring Optimization

This paper analyzes bitstring functions, character-izes genes according to their contribution to the genome's fitness, and proposes an optimization algorithm (G ABO) that uses this characterization for directing the optimization process. We define a gene's contribution as the difference between the genome's fitness when the gene takes a value of 1 and its fitness when the gene takes a value of 0. We characterize a gene as intron-like if it does not contribute to the genome's fitness (zero difference) and as separable-like if its contribution to the fitness of both the genome and genome's complement is the same. Gabo divides genes into two groups coding-like and intron-like genes. Then it searches for an optimal solution by reducing intron-like genes (IOSA) and analyzing coding-like genes (COSA). G Aborepeats these two steps while there are intron-like genes, not all genes are separable-like, and function evaluations are available. We test the performance of Gabo on well-known binary-encoding functions and a function that we define as the mix of them. Our results indicate that G Aboproduces the optimal or near to the optimal solution on the tested functions expending a reduced number of function evaluations and outperforming well-established optimization algorithms.