Enhanced Metaheuristic Algorithms for the Identification of Cancer MDPs | Zendy

Essam Al Daoud | Zendy; Noura Al-Fayoumi | Zendy

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Enhanced Metaheuristic Algorithms for the Identification of Cancer MDPs

Author(s) -

Essam Al Daoud,

Noura Al-Fayoumi

Publication year - 2014

Publication title -

international journal of intelligent systems and applications

Language(s) - English

Resource type - Journals

eISSN - 2074-9058

pISSN - 2074-904X

DOI - 10.5815/ijisa.2014.02.02

Subject(s) - harmony search , computer science , identification (biology) , metaheuristic , genetic algorithm , set (abstract data type) , range (aeronautics) , algorithm , cancer , mathematical optimization , machine learning , artificial intelligence , biology , mathematics , genetics , botany , materials science , composite material , programming language

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.

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