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 Programme
Overview
Detailed
Programme
| Evolutionary
Computation I (ECI) |
CHAIR: ANDREJ DOBNIKAR
Monday, March 21st,
11h00-12h40
| ECI-1 |
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| Title: |
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Evolutionary Design and Evaluation
of Modeling System for Forecasting Urban Airborne Maximum
Pollutant Concentrations |
| Author(s): |
H. Niska,
T. Hiltunen,
A. Karppinen,
M. Kolehmainen |
| Abstract: |
In this paper, a modeling system based on the
combination of a multi-layer perceptron (MLP), a meteorological
pre-processing model and a numerical weather prediction model
(NWP) is developed and evaluated for the forecasting of urban
airborne maximum pollutant concentrations. As an important
phase of the system design, the multi-objective genetic algorithm
(MOGA) and the sensitivity analysis of MLP are used in combination
for identifying feasible system inputs. The final evaluation
of the modeling system is performed by utilizing the hourly
concentrations of nitrogen dioxide (NO2), particulate matter
(PM10), fine particulate matter (PM2.5) and ozone (O3) measured
at an urban air quality station in central Helsinki (capital
of Finland) during the period from 1.5.2000 to 1.5.2003. The
study showed that the combination of MOGA and the sensitivity
analysis is an appropriate tool for selecting inputs of neural
network and can be recommended for wider dissemination and
use. The results showed good general performance of the modeling
system. However, capability to model episodic conditions was
only moderate. |
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| ECI-2 |
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| Title: |
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Evolving Evolvability: Evolving
both representations and operators |
| Author(s): |
Grant W. Braught |
| Abstract: |
The behavior of an evolutionary system incorporating
both an evolving genetic representation (a learning mechanism)
and an evolving genetic operator (mutation) is explored. Simulations
are used to investigate the co-adaptation of these two self-adaptive
mechanisms. The results illustrate a duality between these
two mechanisms in their production of a transmission function.
Further, the adaptive power of the representation is shown
to affect the balance in this duality and to influence the
conservatism of the transmission function. |
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| ECI-3 |
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| Title: |
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A Multi-Objective Evolutionary
Algorithm for Solving Traveling Salesman Problems: Application
to the Design of Polymer Extruders |
| Author(s): |
A. Gaspar-Cunha |
| Abstract: |
A Multi-Objective Evolutionary Algorithm (MOEA)
for solving Traveling Salesman Problems (TSP) was developed
ande used in the design of screws for twin screw polymer extrusion.
This is an important and original contribution in the design
of these machines. The Twin-Screw Configuration Problem (TSCP)
can be formulated as a TSP A different MOEA is developed,
in order to take into account the discrete nature of the TSCP.
The algorithm proposed was applied to some case studies where
the practical usefulness of this approach was demonstrated.
Finally, the computational results are confronted with experimental
data showing the validity of the approach proposed. |
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| ECI-4 |
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| Title: |
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The Pareto-Box Problem for the
Modelling of Evolutionary Multiobjective Optimization Algorithms |
| Author(s): |
Mario Koeppen,
Raul Vicente-Garcia,
Bertram Nickolay |
| Abstract: |
This paper presents the Pareto-Box problem for
modelling evolutionary multi-objective search. The problem
is to find the Pareto set of randomly selected points in the
unit hypercube. While the Pareto set itself is only comprised
of the point 0, this problem allows for a complete analysis
of random search and demonstrates the fact that with increasing
number of objectives, the probability of finding a dominated
vector is decreasing exponentially. Since most nowadays evolutionary
multi-objective optimization algorithms rely on the existence
of dominated individuals, they show poor performance on this
problem. However, the fuzzification of the Pareto-dominance
is an example for an approach that does not need dominated
individuals, thus it is able to solve the Pareto-Box problem
even for a higher number of objectives. |
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