Essam Al Daoud; Noura Al-Fayoumi

Enhanced Metaheuristic Algorithms for the Identification of Cancer MDPs

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Author(s)

Essam Al Daoud ^1,* Noura Al-Fayoumi ¹

1. Department of Computer Science, Zarqa Private University, Zarqa, Jordan

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2014.02.02

Received: 11 May 2013 / Revised: 4 Sep. 2013 / Accepted: 5 Nov. 2013 / Published: 8 Jan. 2014

Index Terms

Genetic Algorithm, Maximum Weight Submatrix, Improved Harmony Search, Mutated Driver Pathways

Abstract

Cancer research revolves around the study of diseases that involve unregulated cell growth. This direction facilitated the development of a wide range of cancer genomics projects that are designed to support the identification of mutated driver pathways in several cancer types. In this research, a maximum weight submatrix problem is used to identify the driver pathway in a specific type of cancer. To solve this problem, we propose two new metaheuristic algorithms. The first is an improved harmony search (IHS) algorithm and the second is an enhanced genetic algorithm (EGA). Results show that EGA enables better performance and entails less computational time than does conventional GA. Furthermore, the new IHS offers a higher number of suggested gene set solutions for mutated genes than does the standard genetic algorithm.

Cite This Paper

Essam Al Daoud, Noura Al-Fayoumi, "Enhanced Metaheuristic Algorithms for the Identification of Cancer MDPs", International Journal of Intelligent Systems and Applications(IJISA), vol.6, no.2, pp.14-21, 2014. DOI:10.5815/ijisa.2014.02.02

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International Journal of Intelligent Systems and Applications (IJISA)