Journal of Computers Vol. 30 No. 5, 2019, pp. 111-127 doi:10.3966/199115992019103005009 111 Multi-offspring Genetic Algorithm with Two-point Crossover and the Relationship between Number of Offsprings and Computational Speed Jiquan Wang 1* , Zhiwen Cheng 1 , Okan K. Ersoy 2 , Panli Zhang 1 , Weiting Dai 1 1 College of Engineering, Northeast Agricultural University, No. 600 ChangJiang Road, Harbin, Heilongjiang, China wangjiquan@neau.edu.cn, {chang1993621, PPenelope, dv15663580893}@163.com 2 Purdue University, School of Electrical and Computer Engineering West Lafayette, Indiana 47907-1285 ersoy@purdue.edu Received 23 November 2017; Revised 31 March 2018; Accepted 2 June 2018 Abstract. This paper presents a multi-offspring genetic algorithm (MGA) with two-point crossover in accordance with biology and mathematical ecological theory. For the MGA, the main existing problems are generation methods of multi-offsprings with different crossover methods, the best number of offsprings and the influence of the number of offsprings on the speed of computation. To solve these problems, the paper first studies the relationship between the number of offsprings and the computational speed of the MGA with two-point crossover. Furthermore, the relationship between the generation method of multi-offsprings, the number of offsprings and the computational speed is analyzed. The results with ten test functions show that when the number of offsprings generated by the MGA based on two-point crossover equals 6, the MGA with two-point crossover has significantly improved the computational speed and reduced the number of iterations as compared to the basic genetic algorithm (BGA) and the MGA of single-point crossover. Keywords: computing speed, multi-offspring genetic algorithm, mutation, offspring individual quantity, two-point crossover 1 Introduction Genetic algorithm (GA) is a random global search optimization technology based on Darwin’s natural evolution theory and Mendel’s genetics and mutation theory [1]. GA was proposed by Professor John H. Holland and his student at Michigan University in the late 1960s and early 1970s [2-6]. De Jong proposed an elitist reserved evolutionary strategy in his doctoral thesis in 1975, and later proposed a variety of evolutionary strategies of elitist retention and selection instead of copying [7-10]; currently, GA usually utilizes this evolutionary strategy. In recent years, GA has attracted more attention because of its unique and superior performance. Many scholars have conducted in-depth studies on GA and proposed improved algorithms, such as hierarchical GA, CHC algorithm, messy GA, self-adaptive GA, GA based on niche technology, hybrid GA, and parallel GA [11-17]. In these studies, common approaches are two parent individuals generating two offsprings, multiple parents generating two offsprings [18-21], and one parent generating one offspring [22]. Thus, the number of offsprings is less than or equal to the number of parents. When the crossover probability equals 1, the number of offsprings equals that of parents; when the crossover probability is less than 1, the number of offsprings is less than that of parents [23-25]. This is unlike the situation in the biosphere for animals and plants for survival in nature. In recent years, some scholars have proposed the concept of multi-offspring genetic algorithm [26-27]. Reference [26] showed the advantages of MGA by * Corresponding Author