633 research outputs found
Genetic Algorithms and Machine Learning
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46947/1/10994_2005_Article_422926.pd
Propeller optimization by interactive genetic algorithms and machine learning
Marine propeller design can be carried out with the aid of automated optimization, but experience shows that a such an approach has still been inferior to manual design in industrial scenarios. In this study, the automated propeller design optimization is evolved by integrating human–computer interaction as an intermediate step. An interactive optimization methodology, based on interactive genetic algorithms (IGAs), has been developed, where the blade designers systematically guide a genetic algorithm towards the objectives. The designers visualize and assess the shape of the blade cavitation and this evaluation is integrated in the optimization method. The IGA is further integrated with a support-vector machine model, in order to avoid user fatigue, IGA\u27s main disadvantage. The results of the present study show that the IGA optimization searches solutions in a more targeted manner and eventually finds more non-dominated feasible designs that also show a good cavitation behaviour in agreement with designer preference
Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case
Astronomy has entered the big data era and Machine Learning based methods
have found widespread use in a large variety of astronomical applications. This
is demonstrated by the recent huge increase in the number of publications
making use of this new approach. The usage of machine learning methods, however
is still far from trivial and many problems still need to be solved. Using the
evaluation of photometric redshifts as a case study, we outline the main
problems and some ongoing efforts to solve them.Comment: 13 pages, 3 figures, Springer's Communications in Computer and
Information Science (CCIS), Vol. 82
Ratiometric control for differentiation of cell populations endowed with synthetic toggle switches
We consider the problem of regulating by means of external control inputs the
ratio of two cell populations. Specifically, we assume that these two cellular
populations are composed of cells belonging to the same strain which embeds
some bistable memory mechanism, e.g. a genetic toggle switch, allowing them to
switch role from one population to another in response to some inputs. We
present three control strategies to regulate the populations' ratio to
arbitrary desired values which take also into account realistic physical and
technological constraints occurring in experimental microfluidic platforms. The
designed controllers are then validated in-silico using stochastic agent-based
simulations.Comment: Accepted to CDC'201
Optimization Study For The Cross-Section Of A Concrete Gravity Dam: Genetic Algorithm Model And Application
Concrete gravity dams have trapezoidal shape in their cross section and shall guarantee the global stability against acting loads like hydrostatic and uplift pressures through his gravitational actions (self-weight and others). This study focuses on the shape optimization of concrete gravity dams using genetic algorithms. In this case, the dam cross section area is considered as the objective function and the design variables are the geometric parameters of the gravity dam. The optimum cross-section of a concrete gravity dam is achieved by the Genetic Algorithm (GA) through a Matlab routine developed by the author. Sliding, overturning and floating verifications are implemented in the program. In order to assess the efficiency of the proposed methodology for gravity dams optimization, one application is presented adopting the concrete gravity dam of Belo Monte Hydropower Plant (HPP), considering normal loading condition and others assumptions presented.Peer Reviewe
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