A review on genetic algorithm: past, present, and future
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A review on genetic algorithm: past, present, and future Sourabh Katoch 1 & Sumit Singh Chauhan 1 & Vijay Kumar 1 Received: 27 July 2020 / Revised: 12 October 2020 / Accepted: 23 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching. Keywords Optimization . Metaheuristic . Genetic algorithm . Crossover . Mutation . Selection . Evolution
1 Introduction In the recent years, metaheuristic algorithms are used to solve real-life complex problems arising from different fields such as economics, engineering, politics, management, and engineering [113]. Intensification and diversification are the key elements of metaheuristic algorithm. The proper balance between these elements are required to solve the real-life problem in an effective manner. Most of metaheuristic algorithms are inspired from biological evolution process, swarm behavior, and physics’ law [17]. These algorithms are broadly classified into two categories namely single solution and population based metaheuristic algorithm (Fig. 1). Single-solution based metaheuristic algorithms utilize single candidate solution and improve this solution by using local search. However, the solution obtained from single-solution based metaheuristics may stuck in local optima [112]. The well-known single-solution based metaheuristics are
* Vijay Kumar [email protected]
1
Computer Science and Engineering Department, National Institute of Technology, Hamirpur, India
Multimedia Tools and Applications Metaheuristics
Single-solution based Metaheuristics
Population based Metaheuristics
Evolutionary Algorithms
Swarm-Intelligence Algorithms
Fig. 1 Classification of metaheuristic Algorithms
simulated annealing, tabu search (TS), microcanonical annealing (MA), and guided local search (GLS). Population-based metaheuristics utilizes multiple candidate solutions during the search process. These metaheuristics maintain the diversity in population and avoid the solutions are being stuck in local optima. Some of well-known population-based metaheuristic algorithms are genetic algorithm (GA) [135], particle swarm optimization (PSO) [101], ant colony optimization (ACO) [47], spotted hyena optimizer (SHO) [41], emperor penguin optimizer (EPO) [42], and seagull optimization (SOA) [43]. Among the metaheuristic algo
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