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What and Who

Multi-Objective Evolutionary Algorithms for Bioinformatics

Luis Enrique Ramirez Chavez
Max-Planck-Institut für Informatik
IMPRS Research Seminar
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI  
AG Audience
English

Date, Time and Location

Monday, 15 June 2015
12:05
20 Minutes
E1 4
024
Saarbrücken

Abstract

In engineering and scientific applications, there exist problems that
involve the simultaneous optimization of several objectives. Usually, such
objectives are conflicting such that no single solution is simultaneously
optimal with respect to all objectives. These types of problems are known
as Multi-objective Optimization Problems (MOPs).

For these more complex optimization problems, the use of meta-heuristics
is fully justified. Multi-Objective Evolutionary Algorithms (MOEAs) are
meta-heuristics which, in recent years, have become very popular because
of their conceptual simplicity and efficiency in these types of problems.
For their nature (based on a population), MOEAs allow to generate multiple
Pareto optimal solutions in a single run. Therefore, nowadays, MOEAs
constitute one of the most successful approaches for solving MOPs.

Numerous of problems encountered in bioinformatics and computational
biology can be formulated as optimization problems and, thus, lend
themselves to the application of powerful heuristic search techniques.
Recently, in biology, Multi-objective optimization has been shown to have
significant benefits compared to single-objective approaches, e.g., in
classification, system optimization and inverse problems.

In this talk I will present some of the MOEAs approaches that have been
used to solve different kind of bioinformatics problems.

Contact

Andrea Ruffing
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Andrea Ruffing, 06/12/2015 15:30 -- Created document.