Institutions
|
About Us
|
Help
|
Gaeilge
0
1000
Home
Browse
Advanced Search
Search History
Marked List
Statistics
A
A
A
Author(s)
Institution
Publication types
Funder
Year
Limited By:
Subject = Evolutionary algorithms;
9 items found
Sort by
Title
Author
Item type
Date
Institution
Peer review status
Language
Order
Ascending
Descending
25
50
100
per page
Bibtex
CSV
EndNote
RefWorks
RIS
XML
Displaying Results 1 - 9 of 9 on page 1 of 1
Marked
Mark
A comparison between two optimisation alternatives for mapping in wireless network on chip
(2016)
Sacanamboy, Maribell; Quesada, Luis; Bolanos, Freddy; Bernal, Alvaro; O'Sullivan, ...
A comparison between two optimisation alternatives for mapping in wireless network on chip
(2016)
Sacanamboy, Maribell; Quesada, Luis; Bolanos, Freddy; Bernal, Alvaro; O'Sullivan, Barry
Abstract:
Network on Chip (NoC) is a well known approach that aims at improving the performance of many-core systems. The design of such systems involves the optimal mapping of tasks to nodes, and the corresponding scheduling of the tasks at every node, which results in a challenging optimisation problem considering the constraints that need to be respected. In this paper, after formalising the problem and elaborating on its complexity, we present an AI approach to solve the problem and evaluate it against a MIP approach. Our empirical evaluation shows that the AI approach is able to obtain solutions of good quality very quickly.
http://hdl.handle.net/10468/5690
Marked
Mark
Arabidopsis thaliana Inspired Genetic Restoration Strategies
(2013)
Hatton, Donagh; O'Donoghue, Diarmuid
Arabidopsis thaliana Inspired Genetic Restoration Strategies
(2013)
Hatton, Donagh; O'Donoghue, Diarmuid
Abstract:
A controversial genetic restoration mechanism has been proposed for the model organism Arabidopsis thaliana. This theory proposes that genetic material from non-parental ancestors is used to restore genetic information that was inadvertently corrupted during reproduction. We evaluate the effectiveness of this strategy by adapting it to an evolutionary algorithm solving two distinct benchmark optimization problems. We compare the performance of the proposed strategy with a number of alternate strategies – including the Mendelian alternative. Included in this comparison are a number of biologically implausible templates that help elucidate likely reasons for the relative performance of the different templates. Results show that the proposed non- Mendelian restoration strategy is highly effective across the range of conditions investigated – significantly outperforming the Mendelian alternative in almost every situation.
http://mural.maynoothuniversity.ie/4490/
Marked
Mark
GeNePi: a multi-objective machine reassignment algorithm for data centres
(2014)
Saber, Takfarinas; Ventresque, Anthony; Gandibleux, Xavier; Murphy, Liam
GeNePi: a multi-objective machine reassignment algorithm for data centres
(2014)
Saber, Takfarinas; Ventresque, Anthony; Gandibleux, Xavier; Murphy, Liam
Abstract:
Data centres are facilities with large amount of machines (i.e., servers) and hosted processes (e.g., virtual machines). Managers of data centres (e.g., operators, capital allocators, CRM) constantly try to optimise them, reassigning `better' machines to processes. These man- agers usually see better/good placements as a combination of distinct objectives, hence why in this paper we de ne the data centre optimisa- tion problem as a multi-objective machine reassignment problem. While classical solutions to address this either do not nd many solutions (e.g., GRASP), do not cover well the search space (e.g., PLS), or even can- not operate properly (e.g., NSGA-II lacks a good initial population), we propose GeNePi, a novel hybrid algorithm. We show that GeNePi out- performs all the other algorithms in terms of quantity of solutions (nearly 6 times more solutions on average than the second best algorithm) and quality (hypervolume of the Pareto frontier is 106% better on average).
http://hdl.handle.net/10344/4361
Marked
Mark
K-Bit-Swap: A New Operator For Real-Coded Evolutionary Algorithms
(2017)
Ter-Sarkisov, Aram; Marsland, Stephen
K-Bit-Swap: A New Operator For Real-Coded Evolutionary Algorithms
(2017)
Ter-Sarkisov, Aram; Marsland, Stephen
Abstract:
There has been a variety of crossover operators proposed for Real-Coded Genetic Algorithms (RCGAs), which recombine values from the same location in pairs of strings. In this article we present a recombination operator for RC- GAs that selects the locations randomly in both parents, and compare it to mainstream crossover operators in a set of experiments on a range of standard multidimensional optimization problems and a clustering problem. We present two variants of the operator, either selecting both bits uniformly at random in the strings, or sampling the second bit from a normal distribution centered at the selected location in the first string. While the operator is biased towards exploitation of fitness space, the random selection of the second bit for swap- ping makes it slightly less exploitation-biased. Extensive statistical analysis using a non-parametric test shows the advantage of the new recombination operators on a range of test functions. K-Bit-Swap: A New Operator Fo...
https://arrow.dit.ie/scschcomart/53
Marked
Mark
Managing Repetition in Grammar-Based Genetic Programming
(2017)
Nicolau, Miguel; Fenton, Michael
Managing Repetition in Grammar-Based Genetic Programming
(2017)
Nicolau, Miguel; Fenton, Michael
Abstract:
Genetic and Evolutionary Computation - GECCO 2016, Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20-24, 2016, Proceedings, July, 2016
Grammar-based Genetic Programming systems are capable of generating identical phenotypic solutions, either by creating repeated genotypic representations, or from distinct genotypes, through their many-to-one mapping process. Furthermore, their initialisation process can generate a high number of duplicate individuals, while traditional variation and replacement operators can permit multiple individuals to percolate through generations unchanged. This can lead to a high number of phenotypically identical individuals within a population. This study investigates the frequency and effect of such duplicate individuals on a suite of benchmark problems. Both Grammatical Evolution and the CFG-GP systems are examined. Experimental evidence suggests that these useless evaluations can be instead be used either to speed-up the evolut...
http://hdl.handle.net/10197/8248
Marked
Mark
Neutrality in evolutionary algorithms... what do we know?
(2012)
Galván-López, Edgar; Poli, Riccardo; Kattan, Ahmed; O'Neill, Michael; Brabazon, An...
Neutrality in evolutionary algorithms... what do we know?
(2012)
Galván-López, Edgar; Poli, Riccardo; Kattan, Ahmed; O'Neill, Michael; Brabazon, Anthony
Abstract:
Over the last years, the effects of neutrality have attracted the attention of many researchers in the Evolutionary Algorithms (EAs) community. A mutation from one gene to another is considered as neutral if this modification does not affect the phenotype. This article provides a general overview on the work carried out on neutrality in EAs. Using as a framework the origin of neutrality and its study in different paradigms of EAs (e.g., Genetic Algorithms, Genetic Programming), we discuss the most significant works and findings on this topic. This work points towards open issues, which the community needs to address.
Science Foundation Ireland
ti, ke, ab, li - TS 02.12 12 month EMBARGO
http://hdl.handle.net/10197/3532
Marked
Mark
Towards a Multi-Objective VM Reassignment for Large Decentralised Data Centres
(2015)
Saber, Takfarinas; Ventresque, Anthony; Brandic, Ivona; Thorburn, James; Murphy, Liam, ...
Towards a Multi-Objective VM Reassignment for Large Decentralised Data Centres
(2015)
Saber, Takfarinas; Ventresque, Anthony; Brandic, Ivona; Thorburn, James; Murphy, Liam, B.E.
Abstract:
2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), Limassol, Cyprus, 7 - 10 December 2015
Optimising the IT infrastructure of large, often geographically distributed, organisations goes beyond the classical virtual machine reassignment problem, for two reasons: (i) the data centres of these organisations are composed of a number of hosting departments which have different preferences on what to host and where to host it; (ii) the top-level managers in these data centres make complex decisions and need to manipulate possible solutions favouring different objectives to find the right balance. This challenge has not yet been comprehensively addressed in the literature and in this paper we demonstrate that a multi-objective VM reassignment is feasible for large decentralised data centres. We show on a realistic data set that our solution outperforms other classical multi-objective algorithms for VM reassignment in terms of quantity of solutions (by abou...
http://hdl.handle.net/10197/7312
Marked
Mark
Validation of a morphogenesis Model of Drosophila Early Development by a Multi-objective evolutionary Optimization Algorithm
(2017)
Dilão, Rui; Muraro, Daniele; Nicolau, Miguel; Schoenauer, Marc
Validation of a morphogenesis Model of Drosophila Early Development by a Multi-objective evolutionary Optimization Algorithm
(2017)
Dilão, Rui; Muraro, Daniele; Nicolau, Miguel; Schoenauer, Marc
Abstract:
7th European Conference: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2009, Tubingen, Germany, 15-17 April 2009
We apply evolutionary computation to calibrate the parameters of a morphogenesis model of Drosophila early development. The model aims to describe the establishment of the steady gradients of Bicoid and Caudal proteins along the antero-posterior axis of the embryo of Drosophila. The model equations consist of a system of non-linear parabolic partial differential equations with initial and zero flux boundary conditions. We compare the results of single- and multi-objective variants of the CMA-ES algorithm for the model the calibration with the experimental data. Whereas the multiobjective algorithm computes a full approximation of the Pareto front, repeated runs of the single-objective algorithm give solutions that dominate (in the Pareto sense) the results of the multi-objective approach. We retain as best solutions those found ...
http://hdl.handle.net/10197/8294
Marked
Mark
Zero is not a Four Letter Word: Studies in the Evolution of Language
(2017)
Stephens, Christopher R.; Nicolau, Miguel; Ryan, Conor
Zero is not a Four Letter Word: Studies in the Evolution of Language
(2017)
Stephens, Christopher R.; Nicolau, Miguel; Ryan, Conor
Abstract:
Genetic Programming, 8th European Conference, EuroGP 2005, Lausanne, Switzerland, 30 March - 1 April 2005
We examine a model genetic system that has features of both genetic programming and genetic regulatory networks, to show how various forms of degeneracy in the genotype-phenotype map can induce complex and subtle behaviour in the dynamics that lead to enhanced evolutionary robustness and can be fruitfully described in terms of an elementary algorithmic 'language'.
http://hdl.handle.net/10197/8275
Displaying Results 1 - 9 of 9 on page 1 of 1
Bibtex
CSV
EndNote
RefWorks
RIS
XML
Institution
Dublin Institute of Technology (1)
Maynooth University (1)
University College Cork (1)
University College Dublin (5)
University of Limerick (1)
Item Type
Conference item (2)
Journal article (3)
Other (4)
Peer Review Status
Peer-reviewed (3)
Unknown (6)
Year
2017 (4)
2016 (1)
2015 (1)
2014 (1)
2013 (1)
2012 (1)
built by Enovation Solutions