16,145 research outputs found
Collaborative Multiobjective Evolutionary Algorithms in search of better Pareto Fronts. An application to trading systems
Technical indicators use graphic representations of data sets by applying
various mathematical formulas to financial time series of prices. These
formulas comprise a set of rules and parameters whose values are not
necessarily known and depend on many factors: the market in which it operates,
the size of the time window, and others. This paper focuses on the real-time
optimization of the parameters applied for analyzing time series of data. In
particular, we optimize the parameters of technical and financial indicators
and propose other applications, such as glucose time series. We propose the
combination of several Multi-objective Evolutionary Algorithms (MOEAs). Unlike
other approaches, this paper applies a set of different MOEAs, collaborating to
construct a global Pareto Set of solutions. Solutions for financial problems
seek high returns with minimal risk. The optimization process is continuous and
occurs at the same frequency as the investment time interval. This technique
permits the application of non-dominated solutions obtained with different
MOEAs simultaneously. Experimental results show that this technique increases
the returns of the commonly used Buy \& Hold strategy and other multi-objective
strategies, even for daily operations
MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities
Urbanism is no longer planned on paper thanks to powerful models and 3D
simulation platforms. However, current work is not open to the public and lacks
an optimisation agent that could help in decision making. This paper describes
the creation of an open-source simulation based on an existing Dutch
liveability score with a built-in AI module. Features are selected using
feature engineering and Random Forests. Then, a modified scoring function is
built based on the former liveability classes. The score is predicted using
Random Forest for regression and achieved a recall of 0.83 with 10-fold
cross-validation. Afterwards, Exploratory Factor Analysis is applied to select
the actions present in the model. The resulting indicators are divided into 5
groups, and 12 actions are generated. The performance of four optimisation
algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three
established criteria of quality: cardinality, the spread of the solutions,
spacing, and the resulting score and number of turns. Although all four
algorithms show different strengths, eps-MOEA is selected to be the most
suitable for this problem. Ultimately, the simulation incorporates the model
and the selected AI module in a GUI written in the Kivy framework for Python.
Tests performed on users show positive responses and encourage further
initiatives towards joining technology and public applications.Comment: 16 page
Optimization as a design strategy. Considerations based on building simulation-assisted experiments about problem decomposition
In this article the most fundamental decomposition-based optimization method
- block coordinate search, based on the sequential decomposition of problems in
subproblems - and building performance simulation programs are used to reason
about a building design process at micro-urban scale and strategies are defined
to make the search more efficient. Cyclic overlapping block coordinate search
is here considered in its double nature of optimization method and surrogate
model (and metaphore) of a sequential design process. Heuristic indicators apt
to support the design of search structures suited to that method are developed
from building-simulation-assisted computational experiments, aimed to choose
the form and position of a small building in a plot. Those indicators link the
sharing of structure between subspaces ("commonality") to recursive
recombination, measured as freshness of the search wake and novelty of the
search moves. The aim of these indicators is to measure the relative
effectiveness of decomposition-based design moves and create efficient block
searches. Implications of a possible use of these indicators in genetic
algorithms are also highlighted.Comment: 48 pages. 12 figures, 3 table
Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context
A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA
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