Adaptation of Kohonen feature map topologies by genetic algorithms

The following paper presents simulational results of coupling Genetic Algorithms to the Kohonen Feature Map paradigm. The Genetic Algorithm is used to improve the Kohonen Net topology, thus yielding better adaptation to the input vector space [0, 1]. Different parameters of the process and their influence as to the resulting topologies are discussed

MSG: A Gap-Oriented Genetic Algorithm for Multiple Sequence Alignment

Traditional Multiple Sequence Alignment (MSA) Algorithms are deterministic. Genetic algorithms for protein MSA have been documented. However, these are not able to exceed in all cases the scores obtained by Clustal­W, the freely available de­facto standard. My solution, called “MSG”, places gaps rather than amino acids. The algorithm is multi­tribal, uses only a few very simple operators with adaptive frequencies, and jumpstarts one population from the Clustal­W solution. Results are reported for 14 data sets, on all of which MSG exceeds the Clustal­W score