Authors:
Mauro Castelli
1
;
Luca Manzoni
2
;
Ivo Gonçalves
3
;
Leonardo Vanneschi
1
;
Leonardo Trujillo
4
and
Sara Silva
5
Affiliations:
1
Universidade Nova de Lisboa, Portugal
;
2
Universitá degli Studi di Milano Bicocca, Italy
;
3
Universidade Nova de Lisboa and University of Coimbra, Portugal
;
4
Instituto Tecnológico de Tijuana, Mexico
;
5
University of Lisbon and University of Coimbra, Portugal
Keyword(s):
Genetic Programming, Semantics, Convex Hull.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
Geometric semantic operators have recently shown their
ability to outperform standard genetic operators on
different complex real world problems. Nonetheless, they
are affected by drawbacks. In this paper, we focus on one
of these drawbacks, i.e. the fact that geometric semantic
crossover has often a poor impact on the evolution.
Geometric semantic crossover creates an offspring whose
semantics stands in the segment joining
the parents (in the semantic space). So, it is intuitive that it is not able to
find, nor reasonably approximate, a globally optimal
solution, unless the semantics of the individuals in the
population ``contains'' the target. In this paper, we
introduce the concept of convex hull of a genetic
programming population and we present a method to calculate
the distance from the target point to the convex hull.
Then, we give experimental evidence of the fact that, in
four different real-life test cases, the target is always
outside the
convex hull. As a consequence, we show that
geometric semantic crossover is not helpful in those cases,
and it is not even able to approximate the population to
the target. Finally, in the last part of the paper, we
propose ideas for future work on how to improve geometric
semantic crossover.
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