Abstract-Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in a population, it becomes challenging to locally exploit the solutions. In order to resolve these issues, the focus is now on reaching equilibrium between the explorative and exploitative features of GA. Therefore, the search process can be prompted to produce suitable GA solutions. This paper begins with an introduction, Section 2 describes the GA exploration and exploitation strategies to locate the optimum solutions. Section 3 and 4 present the lists of some prevalent mutation and crossover operators. This paper concludes that the key issue in developing a GA is to deliver a balance between explorative and exploitative features that complies with the combination of operators in order to produce exceptional performance as a GA as a whole.Index Terms-Crossover operator, mutation operator, exploitation, exploration. I. INTRODUCTIONThe main search operator in Genetic algorithms (GA) is the crossover operator which equally as significant as mutation, selection and coding in GA. The crossover operator functions primarily in the survey of information that is accessible through the search space, which inadvertently improves the behavior of the GA. On another note, mutation is a secondary operator. It functions to alter the genes of the offspring. A mutator will diversify the existing population and this inadvertently allows GAs to exploit promising areas of the search space thus avoiding local solutions [1]. Some of the mutation operators are designed to explicitly overcome certain types of issues over others [2]. The performance among all the comparative of GA operators are easily validated and compared through unbiased test problems from the literature, which are diverse in properties in terms of complexity and modality. This study substantially contributes in reviewing some prevalent mutation and crossover operators. The operators maintain a good balance between explorative and exploitative strategies while manufacturing the optimum GA solutions. II. ACHIEVING EXPLORATION AND EXPLOITATION IN GENETIC ALGORITHMA crossover or mutation can function as an exploration or exploitation operator [3], [4]. Although optimization algorithms with higher degree of exploitation may have Manuscript received August 30, 2016; revised December 8, 2016. Siew Mooi Lim is with University Malaysia of Computer Science and Engineering, Malaysia (e-mail: limsm66@gmail.com).higher convergence speed, the challenge lies in locating the optimal solution and chances are it may not get past a local optimum. On the other hand, algorithms that favor exploration over exploitation might consume more time in locating the global optimum, that is, coincidentally, due to its less sophisticated candidate solutions. A comprehensive survey in exploratio...
This chapter presents an overview of optimization techniques followed by a brief survey on several swarm-based natural inspired algorithms which were introduced in the last decade. These techniques were inspired by the natural processes of plants, foraging behaviors of insects and social behaviors of animals. These swam intelligent methods have been tested on various standard benchmark problems and are capable in solving a wide range of optimization issues including stochastic, robust and dynamic problems.
Online gaming is now a well-known electronic entertainment for people all around the world, especially university students. However, the increasing popularity of online gaming may lead to addiction, a problem that has received significant attention. This study aims to identify the factors relating to online gaming addiction. Based on 118 responses collected from online questionnaires, the bivariate correlation test was employed to examine the relationship between depression, loneliness, motivation for escapism and motivation for achievement with online game addiction. Cohen’s effect size f2 for each path is calculated. The findings show that depression, loneliness, motivation for achievement and motivation for escapism have a high positive correlation with online game addiction with large effect size. Keywords: Online Music Streaming Service, Perceived Value, Tangibility Preference, Music Affinity, Online Music Piracy
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