A parallel implementation of a multi-objective evolutionary algorithm
ΕκδότηςΠανεπιστήμιο Κύπρου, Σχολή Θετικών και Εφαρμοσμένων Επιστημών / University of Cyprus, Faculty of Pure and Applied Sciences
Place of publicationΚύπρος
Google Scholar check
MetadataΕμφάνιση πλήρους εγγραφής
The use of Evolutionary Algorithms (EAs) in difficult problems, where the search space is unknown, urges researches to find ways to exploit their parallel aspect. Multi-objective Evolutionary Algorithms (MOEAs) have features that can be exploited to harness the processing power offered by modern multi-core CPUs. Modern programming languages offer the ability to use threads and processes in order to achieve parallelism that is inherent in multi-core CPUs. This thesis presents a parallel implementation of a MOEA algorithm and its application to the de novo drug design problem. Drug discovery and De novo Drug design is a complex task that has to satisfy a number of conflicting objectives, where a MOEA finds a suitable problem to be used on. Further more such a task needs high amount of execution time. The aim is to minimize this time by the use of a parallel MOEA. The results indicate that using multiple processes that execute independent tasks of a MOEA can reduce significantly the execution time required and maintain comparable solution quality thereby achieving improved performance.