Abstract
This paper introduces the Supervisor Evolutionary Algorithm, a novel technique that allows for self-adapt almost all the internal parameters in parallel distributed client-server genetic programming. This novel adapting mechanism, is itself of an evolutionary nature, so we have a double evolutionary tool. The upper level, as is usual in evolutionary computing, has its own customized selection, crossover, and mutation mechanisms. The lower stage used here is the Brain Project a parallel-distributed software tool for formal modelling of numerical data using a hybrid neural-genetic programming technique. As demonstrated by the experiment reported in this paper, our approach works well adapting continuously its internal parameters.
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Abbreviations
- BIs:
-
Base Individuals
- BNGPA:
-
Base Neuro-GP Algorithm
- BP:
-
Brain Project
- CPC:
-
Client Personal Computer
- CPU:
-
Central Processing Unit
- EC:
-
Evolutionary Computing
- GP:
-
Genetic Programming
- GWC:
-
Game Winning Criterion
- IP:
-
Internet Protocol
- LT:
-
Learning Task
- MIMO:
-
Multi-Input-Multi-Output
- PC:
-
Personal Computer
- SAPC:
-
Self-Adaptive Parameter Control
- SEA:
-
Supervised Evolutionary Algorithm
- SIs:
-
Supervisor Individuals
- SPC:
-
Server Personal Computer
- TCP:
-
Transmission Control Protocol
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Russo, M. A novel technique to self-adapt parameters in parallel/distributed genetic programming. Soft Comput 24, 16885–16894 (2020). https://doi.org/10.1007/s00500-020-04982-w
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DOI: https://doi.org/10.1007/s00500-020-04982-w