A process algebra genetic algorithm

Sertac Karaman; Tal Shima; Emilio Frazzoli

doi:10.1109/TEVC.2011.2160400

A process algebra genetic algorithm

Sertac Karaman, Tal Shima, Emilio Frazzoli

Aerospace Engineering

Technion - Israel Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

A genetic algorithm that utilizes process algebra for coding of solution chromosomes and for defining evolutionary based operators is presented. The algorithm is applicable to mission planning and optimization problems. As an example the high level mission planning for a cooperative group of uninhabited aerial vehicles is investigated. The mission planning problem is cast as an assignment problem, and solutions to the assignment problem are given in the form of chromosomes that are manipulated by evolutionary operators. The evolutionary operators of crossover and mutation are formally defined using the process algebra methodology, along with specific algorithms needed for their execution. The viability of the approach is investigated using simulations and the effectiveness of the algorithm is shown in small, medium, and large scale problems.

Original language	English
Article number	6045330
Pages (from-to)	489-503
Number of pages	15
Journal	IEEE Transactions on Evolutionary Computation
Volume	16
Issue number	4
DOIs	https://doi.org/10.1109/TEVC.2011.2160400
State	Published - 2012

Keywords

Genetic algorithms
UAV task assignment
process algebra

All Science Journal Classification (ASJC) codes

Software
Theoretical Computer Science
Computational Theory and Mathematics

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10.1109/TEVC.2011.2160400

Cite this

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title = "A process algebra genetic algorithm",

abstract = "A genetic algorithm that utilizes process algebra for coding of solution chromosomes and for defining evolutionary based operators is presented. The algorithm is applicable to mission planning and optimization problems. As an example the high level mission planning for a cooperative group of uninhabited aerial vehicles is investigated. The mission planning problem is cast as an assignment problem, and solutions to the assignment problem are given in the form of chromosomes that are manipulated by evolutionary operators. The evolutionary operators of crossover and mutation are formally defined using the process algebra methodology, along with specific algorithms needed for their execution. The viability of the approach is investigated using simulations and the effectiveness of the algorithm is shown in small, medium, and large scale problems.",

keywords = "Genetic algorithms, UAV task assignment, process algebra",

author = "Sertac Karaman and Tal Shima and Emilio Frazzoli",

note = "Funding Information: Manuscript received September 28, 2010; revised January 7, 2011 and March 14, 2011; accepted May 7, 2011. Date of publication October 17, 2011; date of current version July 26, 2012. This work was supported in part by the Michigan/AFRL Collaborative Center on Control Sciences, under AFOSR Grant FA 8650-07-2-3744, and in part by AFOSR, Air Force Material Command, under Grant FA8655-09-1-3066.",

year = "2012",

doi = "10.1109/TEVC.2011.2160400",

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A process algebra genetic algorithm

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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