title: Parallel evolutionary computation in bioinformatics applications.
abstract: A large number of optimization problems within the field of Bioinformatics require methods able to handle its inherent complexity (e.g. NP-hard problems) and also demand increased computational efforts. In this context, the use of parallel architectures is a necessity. In this work, we propose ParJECoLi, a Java based library that offers a large set of metaheuristic methods (such as Evolutionary Algorithms) and also addresses the issue of its efficient execution on a wide range of parallel architectures. The proposed approach focuses on the easiness of use, making the adaptation to distinct parallel environments (multicore, cluster, grid) transparent to the user. Indeed, this work shows how the development of the optimization library can proceed independently of its adaptation for several architectures, making use of Aspect-Oriented Programming. The pluggable nature of parallelism related modules allows the user to easily configure its environment, adding parallelism modules to the base source code when needed. The performance of the platform is validated with two case studies within biological model optimization.
authors.affiliation: Computer Sciences and Technologies Center (CCTC), Universidade do Minho, Dep. Informática - Campus de Gualtar - 4710-057 Braga, Portugal. jmpinho@di.uminho.pt
authors.forename: Jorge
authors.initials: J
authors.lastname: Pinho
authors.forename: João Luis
authors.initials: JL
authors.lastname: Sobral
authors.forename: Miguel
authors.initials: M
authors.lastname: Rocha
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003199:Computer Systems; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D005285:Fermentation; D060847:Metabolic Engineering; D011381:Programming Languages; D015203:Reproducibility of Results; D012984:Software; D014584:User-Computer Interface