IMPROVING A GREEDY DNA MOTIF SEARCH USING A MULTIPLE GENOMIC SELF ADAPTATING GENETIC ALGORITHM
We consider the problem of combining a greedy motif search algorithm [16] with a self adapting genetic algorithm employing multiple genomic representations in order to find high scoring substring patterns of size k in a set of t DNA sequences of size n thus improving the results of a stand alone greedy motif search. We propose using multiple genomic (redundant) representations in a self adapting genetic algorithm (GA) employing various codes with different locality properties. These encoding schemes insure feasibility after performing the operations of crossover and mutation and also ensure the feasibility of the initial randomly generated population (i.e. generation 0) The GAs applied in solving this problem employ non locality or locality representations when appropriate (i.e. the GA adapts to its current search needs) which makes the GAs more efficient [15]