Genetic Algorithm Based on Natural Selection Theory for Optimization Problems

Abstract

The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two assessments of the GABONST are carried out via (i) application of fifteen renowned benchmark test functions and the comparison of the results with the conventional GA, enhanced ameliorated teaching learning-based optimization (EATLBO), Bat and Bee algorithms. (ii) Apply the GABONST in language identification (LID) through integrating the GABONST with extreme learning machine (ELM) and named (GABONST-ELM). The ELM is considered as one of the most useful learning models for carrying out classifications and regression analysis. The generation of results is carried out grounded upon the LID dataset, which is derived from eight separate languages. The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment. Additionally, the obtained results indicate that (GABONST-ELM)-LID has an effective performance with accuracy reaching up to 99.38%.

Analysis of the Optimal Application of Blockchain-Based Smart Lockers in the Logistics Industry Based on FFD-SAGA and Grey Decision-Making

“…The simulated annealing-genetic algorithm, which is a hybrid of the genetic algorithm and the simulated annealing method, as shown in the Figure 2, is proposed in this paper [3,15,16]. It improves the ability of the genetic algorithm to search for regions by using the characteristics of the simulated annealing method, which can make small disturbances in adjacent regions of the solution [22,23]…”

“…This paper refers to it as fractional factorial design-simulated annealing (FFD-SA). FFD-SA improves the traditional SA without systematic random disturbances, but uses systematic disturbances to improve the search performance of the algorithm (subsection (3), steps 3-5, below) [3,15,16,22,23]. By combining the fractional factor design method with the mating operator of the simulated annealing-genetic algorithm, the global optimal solution can be obtained.…”

“…FFD-SAGA can be regarded as FFD-GA combined with SA because of the added fractional factorial design method (in the same way as FFD-GA). The steps of the simulated annealing-genetic algorithm by fractional factorial design are as follows [3,15,16,22,23]: Mon :…”

“…The level values of the factors corresponding to the main effects with a larger contribution in each column make up the combination of optimal factors. This paper uses the following example to illustrate FFD-SAGA and how to apply FFD to infer a new solution [3,15,16,22,23].…”

Hyper-Angle Exploitative Searching for Enabling Multi-Objective Optimization of Fog Computing

“…Multi-Objective Optimization (MOO) algorithms [ 15 , 16 ] aim at optimizing many objectives’ functions using heuristic random searching in order to find a set of non-dominated solutions [ 17 ]. There is a high similarity between single objective [ 18 ] and multi-objective meta-heuristics [ 19 , 20 ] in the aspect of relying on a random pool of generated solutions, evaluating them, and selecting the best among them to generate off-spring. However, the essential difference between the single objective and multi-objective heuristic searching is the means of evaluating solutions.…”