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Author: Ghodrat Moghadampour

Affiliation: VAMK, University of Applied Sciences and Technology and Communication, Finland

ISBN: 978-989-8565-10-5

ISSN: 2184-4992

Keyword(s): Evolutionary Algorithm, Genetic Algorithm, Function Optimization, Mutation Operator, Multipoint Mutation Operator, Polymorphic Random Building Block Operator, Fitness Evaluation and Analysis.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence and Decision Support Systems ; Enterprise Information Systems ; Evolutionary Programming

Abstract: Boosting the evolutionary process of genetic algorithms by generating better individuals, avoiding stagnation at local optima and refreshing population in a desirable way is a challenging task. Typically operators are used to achieve these objectives. On the other hand using operators can become a challenging task in itself if applying them requires setting many parameters through human intervention. Therefore, developing operators, which do not require human intervention and at the same time are capable of assisting the evolutionary process, is highly desirable. Most typical genetic operators are mutation and crossover. However, experience has proved that these operators in their classical form are not capable of refining the population efficiently enough. In this work a new dynamic mutation operator called polymorphic random building block operator with variable mutation rate is proposed. This operator does not require any pre-fixed parameter. It randomly selects a section from the binary presentation of the individual, then generates a random bit-string of the same length as the selected section and applies bitwise logical AND, OR and XOR operators between the randomly generated bit-string and the selected section from the individual. In the next step all three newly generated offspring will go through selection procedure and will replace a possibly worse individual in the population. Experimentation with 33 test functions and 11550 test runs proved the superiority of the proposed dynamic mutation operator over single-point mutation operator with 1%, 5% and 8% mutation rates and the multipoint mutation operator with 5%, 8% and 15% mutation rates. (More)

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Paper citation in several formats:
Moghadampour, G. (2012). Polymorphic Random Building Block Operator for Genetic Algorithms.In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8565-10-5, ISSN 2184-4992, pages 342-348. DOI: 10.5220/0004014103420348

author={Moghadampour, G.},
title={Polymorphic Random Building Block Operator for Genetic Algorithms},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},


JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Polymorphic Random Building Block Operator for Genetic Algorithms
SN - 978-989-8565-10-5
AU - Moghadampour, G.
PY - 2012
SP - 342
EP - 348
DO - 10.5220/0004014103420348

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