A genetic algorithm for multiple sequence alignment
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Iterative prototype optimisation with evolved improvement steps
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Improved genetic algorithm for multiple sequence alignment using segment profiles (GASP)
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
HGA-COFFEE: aligning multiple sequences by hybrid genetic algorithm
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Efficient stochastic local search algorithm for solving the shortest common supersequence problem
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Black-box optimization benchmarking of two variants of the POEMS algorithm on the noiseless testbed
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Software project portfolio optimization with advanced multiobjective evolutionary algorithms
Applied Soft Computing
Context-sensitive refinements for stochastic optimisation algorithms in inductive logic programming
Artificial Intelligence Review
Hi-index | 0.00 |
This paper deals with a Multiple Sequence Alignment problem, for which an implementation of the Prototype Optimization with Evolved Improvement Steps (POEMS) algorithm has been proposed. The key feature of the POEMS is that it takes some initial solution, which is then iteratively improved by means of what we call evolved hypermutations. In this work, the POEMS is seeded with a solution provided by the Clustal X algorithm. Major result of the presented experiments was that the proposed POEMS implementation performs significantly better than the other two compared algorithms, which rely on randomhypermutations only. Based on the carried out analyses we proposed two modifications of the POEMS algorithm that might further improve its performance.