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 Programme
Overview
Detailed
Programme
| Genetic Algorithms
Theory (GAT) |
CHAIR: COLIN REEVES
Monday, March 21st,
16h30-18h10
| GAT-1 |
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| Title: |
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Offspring Selection: A New Self-Adaptive
Selection Scheme for Genetic Algorithms |
| Author(s): |
M. Affenzeller,
S. Wagner |
| Abstract: |
In terms of goal orientedness, selection is
the driving force of Genetic Algorithms (GAs). In contrast
to crossover and mutation, selection is completely generic,
i.e. independent of the actually employed problem and its
epresentation. GA-selection is usually implemented as selection
for reproduction (parent selection). In this paper we propose
a second selection step after reproduction which is also absolutely
problem independent. This self-adaptive selection mechanism,
which will be referred to as offspring selection, is closely
related to the general selection model of population genetics.
As the problem- and representation-specific implementation
of reproduction in GAs (crossover) is often critical in terms
of preservation of essential genetic information, offspring
selection has proven to be very suited for improving the global
solution quality and robustness concerning parameter settings
and operators of GAs in various fields of applications. The
experimental part of the paper discusses the potential of
the new selection model exemplarily on the basis of standardized
real-valued test functions in high dimensions. |
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| GAT-2 |
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| Title: |
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Using Genetic Algorithms with Real-coded
Binary Representation for Solving Non-stationary Problems |
| Author(s): |
Jiri Kubalik |
| Abstract: |
This paper presents genetic algorithms with
real-coded binary representation - a novel approach to improve
the performance of genetic algorithms. The algorithm is capable
of maintaining the diversity of the evolved population during
the whole run which protects it from premature convergence.
This is achieved by using a special encoding scheme, introducing
a high redundancy, which is further supported by so-called
gene-strength adaptation mechanism for controlling the population
diversity. Its capability of self-regulation of the population
diversity makes the algorithm robust for solving non-stationary
problems as it was empirically proven on two test cases. Achieved
results show the proposed genetic algorithm is competitive
with other techniques designed for solving non-stationary
problems. |
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| GAT-3 |
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| Title: |
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Dynamics in Proportionate Selection |
| Author(s): |
Abhishek Agrawal,
Ian Mitchell,
Peter Passmore,
Ivan Litovski |
| Abstract: |
This paper proposes a new selection method for
Genetic Algorithms. The new method Dynamic Selection Method
(DSM) is based on proportionate selection. DSM functions by
continuously changing the criteria for parent selection (dynamic)
based on the number of generations in a run and the current
generation. Results show that by using DSM to maintain diversity
in a population gives slower convergence, but, their overall
performance was an improvement. Relationship between slower
convergences, in GA runs, leading to better solutions, has
been identified. |
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| GAT-4 |
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| Title: |
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Generating grammatical plant models
with genetic algorithms |
| Author(s): |
Luis E. Da Costa,
Jacques-André Landry |
| Abstract: |
A method for synthesizing grammatical models
of natural plants is presented in this paper. A geometric
study is undertaken before translating it into grammatical
meaning; a suite of genetic algorithms is then applied for
navigating through the space of possible solutions. Preliminary
results, together with a detailed description of the method,
are presented. |
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