Hybrid Evolutionary Search Method Based on Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A computerized causal forecasting system using genetic algorithms in supply chain management
Journal of Systems and Software
Journal of Intelligent and Robotic Systems
International Journal of Systems Science
Theoretical Analysis of Mutation-Adaptive Evolutionary Algorithms
Evolutionary Computation
Space Complexity of Estimation of Distribution Algorithms
Evolutionary Computation
The second largest eigenvalue upper bound for the canonical genetic algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hybrid solution algorithms for task scheduling problem with moving executors
Engineering Applications of Artificial Intelligence
An improved unsupervised clustering algorithm based on population Markov chain
International Journal of Computers and Applications
Evolutionary humanoid robotics: past, present and future
50 years of artificial intelligence
Artificial bee colony algorithm for small signal model parameter extraction of MESFET
Engineering Applications of Artificial Intelligence
IEEE Transactions on Evolutionary Computation
Center Based Genetic Algorithm and its application to the stiffness equivalence of the aircraft wing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid genetic algorithm for the discrete time-cost trade-off problem
Expert Systems with Applications: An International Journal
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
Transactions on Computational Collective Intelligence IX
Binary Accelerated Particle Swarm Algorithm (BAPSA) for discrete optimization problems
Journal of Global Optimization
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In this paper, a concept of degree of population diversity is introduced to quantitatively characterize and theoretically analyze the problem of premature convergence in genetic algorithms (GAs) within the framework of Markov chain. Under the assumption that the mutation probability is zero, the search ability of GA is discussed. It is proved that the degree of population diversity converges to zero with probability one so that the search ability of a GA decreases and premature convergence occurs. Moreover, an explicit formula for the conditional probability of allele loss at a certain bit position is established to show the relationships between premature convergence and the GA parameters, such as population size, mutation probability, and some population statistics. The formula also partly answers the questions of to where a GA most likely converges. The theoretical results are all supported by the simulation experiments