Auto-Poietic Algorithm for Multiple Sequence Alignment

The concept of self-organization is applied to the operators and parameters of genetic algorithm to develop a novel Auto-poietic algorithm solving a biological problem, Multiple Sequence Alignment (MSA). The self-organizing crossover operator of the developed algorithm undergoes a swap and shuffle process to alter the genes of chromosomes in order to produce better combinations. Unlike Standard Genetic Algorithms (SGA), the mutation rate of auto-poietic algorithm is not fixed. The mutation rate varies cyclically based on the improvement of fitness value in turn, determines the termination point of algorithm. Automated assignment of various parameter values reduces the intervention and inappropriate settings of parameters from user without prior the knowledge of input. As an advantage, the proposed algorithm also circumvents the major issues in standard genetic algorithm, premature convergence and time requirements to optimize the parameters. Using Benchmark Alignment Database (BAliBASE) reference multiple sequence alignments, the efficiency of the auto-poietic algorithm is analyzed. It is evident that the performance of auto-poietic algorithm is better than SGA and produces better alignments compared to other MSA tools.