Books by Giovanni Lizarraga Lizarraga
There are many published multi--objective evolutionary algorithms (also known as MOEAs). A natura... more There are many published multi--objective evolutionary algorithms (also known as MOEAs). A natural
question is what algorithm performs better? If we ignore other factors such as
computational complexity, the evaluation of the performance of a MOEA only depends on the output of the algorithm. The output of a MOEA is a set of vectors (usually
known as non--dominated sets) with some special properties derived from the Pareto
Optimality Criteria (POC). Unfortunately, evaluating and comparing these non--dominated sets is not an
easy task and is an open research problem.
Many performance measures have been proposed in the past, but they are sensitive to misleading cases
and sometimes, hard to use. Some theoretical studies have been developed
in order to determine what we want from a good performance measure. Unfortunately, many of the
performance measures derived from those studies are too conservative and have a very
limited capacity to distinguish between good and bad sets.
The goal of this thesis is to analyze the problem and introduce a new method for the evaluation of non--dominated sets, named G--Ranker. The G--Ranker is designed having in mind most of the desired properties of a non--dominated set, needs no extra information about the multi--objective problem and is more robust to misleading cases compared to other methods.
Also, we introduce a set of test cases to evaluate the effectiveness of a performance
measure. The results of the experiments demonstrate the superiority of the G--Ranker
with respect to state--of--the--art approaches reported in specialized literature.
Papers by Giovanni Lizarraga Lizarraga
Handling Constraints using Multiobjective Optimization Concepts
Handling Constraints using Multiobjective Optimization Concepts
Why Unary Quality Indicators are not Inferior to Binary Quality Indicator
Some Demonstrations about the Cardinality of Important Sets of Non-dominated Sets
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Books by Giovanni Lizarraga Lizarraga
question is what algorithm performs better? If we ignore other factors such as
computational complexity, the evaluation of the performance of a MOEA only depends on the output of the algorithm. The output of a MOEA is a set of vectors (usually
known as non--dominated sets) with some special properties derived from the Pareto
Optimality Criteria (POC). Unfortunately, evaluating and comparing these non--dominated sets is not an
easy task and is an open research problem.
Many performance measures have been proposed in the past, but they are sensitive to misleading cases
and sometimes, hard to use. Some theoretical studies have been developed
in order to determine what we want from a good performance measure. Unfortunately, many of the
performance measures derived from those studies are too conservative and have a very
limited capacity to distinguish between good and bad sets.
The goal of this thesis is to analyze the problem and introduce a new method for the evaluation of non--dominated sets, named G--Ranker. The G--Ranker is designed having in mind most of the desired properties of a non--dominated set, needs no extra information about the multi--objective problem and is more robust to misleading cases compared to other methods.
Also, we introduce a set of test cases to evaluate the effectiveness of a performance
measure. The results of the experiments demonstrate the superiority of the G--Ranker
with respect to state--of--the--art approaches reported in specialized literature.
Papers by Giovanni Lizarraga Lizarraga