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 Programme
Overview
Detailed
Programme
| Complex Systems
and Genetic Programming (CSGP) |
CHAIR: ERNESTO COSTA
Tuesday,
March 22nd, 11h00-12h40
| CSGP-1 |
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| Title: |
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Golomb Rulers: Experiments with
Marks Representation |
| Author(s): |
Jorge Tavares,
Francisco B. Pereira,
Ernesto Costa |
| Abstract: |
We present a set of experiments regarding an evolutionary
algorithm designed to efficiently search for Optimal Golomb
Rulers. The approach uses a binary representation to codify
the marks contained in a ruler and relies on standard genetic
operators. Furthermore, during evaluation, an insertion and
correction procedure is implemented in order to improve the
algorithm performance. This method is successful in quickly
identifying good solutions, outperforming previous evolutionary
approaches by discovering optimal and near-optimal Golomb
Rulers. |
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| CSGP-2 |
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| Title: |
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Evolving Segments Length in Golomb
Rulers |
| Author(s): |
Jorge Tavares,
Tiago Leitao,
Francisco B. Pereira,
Ernesto Costa |
| Abstract: |
An evolutionary algorithm based on Random Keys
to represent Golomb Rulers segments, has been found to be
a reliable option for finding Optimal Golomb Rulers in a short
amount of time, when comparing with standard methods. This
paper presents a modified version of this evolutionary algorithm
where the maximum segment length for a Golomb Ruler is also
part of the evolutionary process. Attained experimental results
shows us that this alteration does not seems to provide significant
benefits to the static version of the algorithm. |
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| CSGP-3 |
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| Title: |
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Resource-Limited Genetic Programming:
Replacing Tree Depth Limits |
| Author(s): |
Sara Silva,
Pedro J.N. Silva,
Ernesto Costa |
| Abstract: |
We propose replacing the traditional tree depth
limit in Genetic Programming by a single limit on the amount
of resources available to the whole population, where resources
are the tree nodes. The resource-limited technique removes
the disadvantages of using depth limits at the individual
level, while introducing automatic population resizing, a
natural side-effect of using an approach at the population
level. The results show that the replacement of individual
depth limits by a population resource limit can be done without
impairing performance, thus validating this first and important
step towards a new approach to improving the efficiency of
GP. |
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| CSGP-4 |
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| Title: |
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Treating some constraints as hard
speeds up the ESG local search Algorithm |
| Author(s): |
Y. Kilani,
A. MohdZin |
| Abstract: |
Local search (LS) methods for solving constraint
satisfaction problems (CSP) such as GSAT, WalkSAT and DLM
starts the search for a solution from a random assignment.
LS then examines the neighbours of this assignment, using
the penalty function to determine a better neighbour valuations
to move to. It repeats this process until it finds a solution
that satisfies all constraints. ICM considers some of the
constraints as hard constraints that are always satisfied.
In this way, the constraints reduce the possible neighbours
in each move and hence the overall search space. We choose
the hard constraints in such away that the space of valuations
that satisfies these constraints is connected in order to
guarantee that a local search can reach any solution from
any valuation in this space. In this paper, we incorporate
ICM into one of the most recent local search algorithm ESG
and we show the improvement of the new algorithm. |
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