Paper Info
Title
A Hyper-heuristic approach towards mitigating Premature Convergence caused by the objective fitness function in GP
Abstract
This manuscript proposes a hyper-heuristic approach towards mitigating Premature Convergence caused by objective fitness in Genetic Programming (GP). The objective fitness function used in standard GP has the potential to profoundly exacerbate Premature Convergence in the algorithm. Accordingly several alternative fitness measures have been proposed in GP literature. These alternative fitness measures replace the objective function, with the specific aim of mitigating this type of Premature Convergence. However each alternative fitness measure is found to have its own intrinsic limitations. To this end the proposed approach automates the selection of distinct fitness measures during the progression of GP. The power of this methodology lies in the ability to compensate for the weaknesses of each fitness measure by automating the selection of the best alternative fitness measure. Our hyper-heuristic approach is found to achieve generality in the alleviation of Premature Convergence caused by objective fitness. Vitally the approach is unprecedented and highlights a new paradigm in the design of GP systems.
Year
DOI
Venue
2014
10.1109/ISDA.2014.7066272
Intelligent Systems Design and Applications
Keywords
Field
DocType
genetic algorithms,GP systems,genetic programming,hyper-heuristic approach,objective fitness function,premature convergence mitigation,Algorithm design and analysis,Evolutionary Computation,Genetic Algorithm,Genetic Programming,Heuristic Algorithms
Mathematical optimization,Algorithm design,Premature convergence,Computer science,Fitness proportionate selection,Fitness function,Genetic programming,Hyper-heuristic,Fitness approximation,Artificial intelligence,Genetic algorithm,Machine learning
Conference
ISSN
Citations 
PageRank 
2164-7143
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Anisa W. Ragalo100.34
Nelishia Pillay223733.72