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.
Original language | English |
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Title of host publication | ECTA 2016 - 8th International Conference on Evolutionary Computation Theory and Applications |
Publisher | SciTePress - Science and Technology Publications |
Pages | 201-208 |
Number of pages | 8 |
Volume | 1 |
ISBN (Electronic) | 9789897582011 |
DOIs | |
Publication status | Published - 2016 |
Event | 8th International Joint Conference on Computational Intelligence, IJCCI 2016 - Porto, Portugal Duration: 9 Nov 2016 → 11 Nov 2016 |
Conference
Conference | 8th International Joint Conference on Computational Intelligence, IJCCI 2016 |
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Country/Territory | Portugal |
City | Porto |
Period | 9/11/16 → 11/11/16 |
Keywords
- Convex hull
- Genetic programming
- Semantics