An innovative framework for designing genetic algorithm structures. (30th December 2017)

• Record Type: Journal Article
• Authors: Dao, Son Duy; Abhary, Kazem; Marian, Romeo
• Is Part Of: Expert systems with applications. Volume 90(2017)
• Page Start: 196
• Page End: 208
• Publication Date: 2017-12-30
• Subjects: Innovative genetic algorithm structure -- Global optimization -- Adaptive restarting mechanism -- Strategy for generating local solutions -- Exploration and exploitation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.08.018
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The proposed GA is capable of automatically restarting its search process if being trapped in local optima. The exploitation of the proposed GA is enhanced by the developed local solution generation module. The search capability of the proposed GA is improved by balancing the exploration and exploitation using Taguchi method. Abstract: Genetic Algorithms are popular optimization algorithms, often used to solve complex large scale optimization problems in many fields. Like other meta-heuristic algorithms, Genetic Algorithms can only provide a probabilistic guarantee of the global optimal solution. Having a Genetic Algorithm (GA) capable of finding the global optimal solution with high success probability is always desirable. In this article, an innovative framework for designing an effective GA structure that can enhance the GA's success probability of finding the global optimal solution is proposed. The GA designed with the proposed framework has three innovations. First, the GA is capable of restarting its search process, based on adaptive condition, to jump out of local optima, if being trapped, to enhance the GA's exploration. Second, the GA has a local solution generation module which is integrated in the GA loop to enhance the GA's exploitation. Third, a systematic method based on Taguchi Experimental Design is proposed to tune the GA parameter set to balance the exploration and exploitation to enhance the GA capability of finding the global optimal solution. Effectiveness of the proposed framework is validated in 20 large-scale case study problems in which the GA designed by the proposed framework always outperforms five other algorithms available in the global optimization literature.