- author = "J. Manuel Colmenar and Jose L. Risco-Martin and David Atienza and J. Ignacio Hidalgo",
- title = "Multi-objective optimization of dynamic memory managers using grammatical evolution",
- booktitle = "GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation",
- year = "2011",
- editor = "Natalio Krasnogor and Pier Luca Lanzi and Andries Engelbrecht and David Pelta and Carlos Gershenson and Giovanni Squillero and Alex Freitas and Marylyn Ritchie and Mike Preuss and Christian Gagne and Yew Soon Ong and Guenther Raidl and Marcus Gallager and Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and Nikolaus Hansen and Silja Meyer-Nieberg and Jim Smith and Gus Eiben and Ester Bernado-Mansilla and Will Browne and Lee Spector and Tina Yu and Jeff Clune and Greg Hornby and Man-Leung Wong and Pierre Collet and Steve Gustafson and Jean-Paul Watson and Moshe Sipper and Simon Poulding and Gabriela Ochoa and Marc Schoenauer and Carsten Witt and Anne Auger",
- isbn13 = "978-1-4503-0557-0",
- pages = "1819--1826",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, genetic improvement, SBSE, Real world applications",
- month = "12-16 " # jul,
- organisation = "SIGEVO",
- address = "Dublin, Ireland",
-
DOI = "
doi:10.1145/2001576.2001820",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- size = "8 pages",
- abstract = "The dynamic memory manager (DMM) is a key element whose customization for a target application reports great benefits in terms of execution time, memory usage and energy consumption. Previous works presented algorithms to automatically obtain custom DMMs for a given application. Nevertheless, those approaches are based on grammatical evolution where the fitness is built as an aggregate objective function, which does not completely exploit the search space, returning the designer the DMM solution with best fitness. However, this approach may not find solutions that could fit in a concrete hardware platform due to a very low value of one of the objectives while the others remain high, which may represent a high fitness. In this work we present the first multi-objective optimisation methodology applied to DMM optimisation where the Pareto dominance is considered, thus providing the designer with a set of non-dominated DMM implementations on each optimisation run. Our results show that the multi-objective optimisation provides Pareto-optimal alternatives due to a better exploitation of the search space obtaining better hypervolume values than the aggregate objective function approach.",
-
notes = "Garbage collector. Energy consumption. NSGA-2.
Pop=40.
Also known as \cite{2001820} GECCO-2011 A joint meeting of the twentieth international conference on genetic algorithms (ICGA-2011) and the sixteenth annual genetic programming conference (GP-2011)",
