Dunwei Gong, Xiaoyan Sun, Xijin Guo et al. · 2004
Contraposed with insufficiency of the simple hierarchy genetic algorithm, the concept of individual density is first proposed in this paper to scale the diversity of population. Combined with individual density and evolution time, the adaptive hierarchy genetic algorithm is put forward. Not only can it improve the diversity of the evolution population, but also ensure rapid convergence. Application examples show its validity.
Cited by 0
Dunwei Gong, Xiaoyan Sun, Win Guo et al. · 2005
Contraposed with insufficiency of the simple hierarchy genetic algorithm, the concept of individual density is first proposed in this paper to scale the diversity of population. Combined with individual density and evolution time, the adaptive hierarchy genetic algorithm is put forward. Not only can it improve the diversity of the evolution population, but also ensure rapid convergence. Application examples show its validity.
Cited by 5
CUHK Digital Repository (Chinese University of Hong Kong) · 2000
Cited by 0
Fulin Wang, Huixia Zhu, Jiquan Wang · International Journal of Advancements in Computing Technology · 2012
Cited by 1
Tang Hongcheng · Advances in intelligent and soft computing · 2012
Cited by 36
Masaharu Munetomo · 2001
This chapter covers: Introduction Routing algorithms in the internet GAs in network resource management Overview of the GARA Path genetic operators Maintaining routing tables Fitness evaluation Execution flow Empirical results Conclusions
Cited by 3
Xiaohua Zeng (793632), Jieping Cai (13262351), Changzhou Liang (13262354) et al. · Figshare · 2022
Cited by 0Open access
LIM KIM HEE FELIX · National University of Singapore · 1999
Cited by 0
Ek Peng Chew, Chong Jin Ong, Kim Hee Lim · Computers & Industrial Engineering · 2002
Cited by 17
Youchan Zhu, Feng Shen · 2012
In order to solve the premature convergence and local optimization and improve the global optimum and fast convergence, a new adaptive genetic algorithm is presented in this paper. It is based on the evaluation of population diversity and calculation of fitness value. The experimental result shows that the algorithm is clearly improved in aspects of convergent speed and stability and gets the desired results.
Cited by 2
Analysis Cristian Munteanu, Cristian Munteanu, Agostinho Rosa · 2007
This paper extends the theoretical analysis of the Adaptive Reservoir Genetic Algorithm (ARGA), a variant of a Genetic Algorithm (GA) proposed by the authors in [4]. We show that ARGA visits the global optimum after a finite number of iterations with probability one, regardless of the initialization of the population.
Cited by 0
Dunwei Gong, Fengping Pan, Xu Shifan · 2004
The niche technology and hierarchy genetic algorithm are combined in this paper. Adaptive hierarchy genetic algorithm based on niche sharing and crowding strategy is presented. The algorithm improves genetic algorithm from not only encoding method but also operators. Thus it has strong search ability and it is easy to find many global optimums. Simulation results validate its efficiency.
Cited by 15
Nga Lam Law · 2007
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Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2007
Parameter-free adaptive genetic algorithm by Nga Lam Law thesis 2007 xvi, 94 leaves : ill. ; 30 cm A new adaptive approach, which applies the concept of relative importance of information to the genetic algorithm, is developed. Selection, crossover and…Read more ›
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Shifen Han, Li Xiao · SHS Web of Conferences · 2022
Genetic algorithm is a classic intelligent bionic algorithm, which is evolved according to the genetic evolution process of organisms in nature, and has strong global optimization ability. Firstly, this paper expounds the basic principle of genetic algorithm, which comes from the viewpoint of “survival of the fittest and survival of the fittest” in Darwin’s theory of evolution, introduces the main characteristics of the algorithm, and summarizes the shortcomings of the algorithm. Based on the specific running steps of genetic algorithm, aiming at the shortcomings of genetic algorithm, an improved adaptive genetic algorithm is proposed. Finally, an example is used for simulation. The simulation results show that the improved algorithm has certain advantages.
Cited by 81Open access
Domagoj Jakobovic · 1997
This paper describes a variant of Genetic Algorithm (GA) with adaptive probabilities of crossover and mutation. The application of GA presented here refers to multimodal function optimization and some deceptive problems. Adaptive probabilities do not, however, restrict the applicability in any way. The adaptive method is designed for steady-state GA with elimination selection. The main idea of the method lies in selective choosing of solutions to be crossed and in varying number of mutations. The adaptive GA is analyzed concerning its capability of locating the global optimum in a multimodal landscape. Several test functions with various degrees of complexity, employed earlier for comparative studies, have been optimized. The performance of standard steady-state GA is compared with that of the adaptive GA. A notable improvement can be perceived in the results: the adaptive GA converges faster and gets stuck at a local optimum fewer times. There is also less preparation needed for the user to get the algorithm to work, as it calculates some of its control parameters itself.
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Soon Lee Lai, Mohammad Jalali Varnamkhasti · Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) · 2009
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Applied To Transonic, Akira Oyama, Shigeru Obayashi et al. · 2001
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Spyros Papadimitriou, Konstantina Chrysafiadi, Maria Virvou · 2023
The quizzes allow the tutor to assess the student's knowledge level briefly. However, creating a quiz is a challenging task that requires time and effort. If the tutor wants to create multiple quizzes tailored to the student's needs, the time needed to construct them increases dramatically. Furthermore, a large pool of questions and activities makes creating personalised quizzes more complex. A solution to the above is employing genetic algorithms to quickly and automatically produce personalised quizzes and tests. However, a genetic algorithm uses complex mathematical forms, which tutors of various knowledge domains find difficult to understand. Given this, the paper presents a genetic algorithm that embeds a fuzzy rule-based mechanism in the fitness calculation phase to select the most appropriate questions for each learner and create personalised quizzes more realistically. The presented algorithm aims to describe with linguistic terms the appropriateness of each question rather than obscure mathematics forms. In such a way, estimating the suitability of a quiz question is closer to the human way of thinking and becomes more explainable to tutors. The presented fuzzy genetic algorithm has been performed for creating quizzes for learners of the programming language HTML. The algorithm accepts a pool of questions and the learner's characteristics and needs. It gives as output a report, which includes the questions sorted by their suitability and a recommended quiz that best fits the learner's needs.
Cited by 5
Chunni Dai, Xiang Wu, Jingao Liu · 2008
Stereo correspondence is the central problem of stereovision. In this paper a new stereo matching scheme using adaptive genetic algorithm is proposed to implement disparity maps to realize depth reconstruction of 3D information. Adaptive crossover and mutation mechanism are employed in this technique. Simulation results show that the performance of the proposed algorithm is better than those get by a fixed-size window.
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