Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization
Expert Systems with Applications: An International Journal
Parameter identification of chaotic systems using evolutionary programming approach
Expert Systems with Applications: An International Journal
Decoupled adaptive neuro-fuzzy (DANF) sliding mode control system for a Lorenz chaotic problem
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
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Evolutionary algorithms (EAs) refer to a broad class of optimization algorithms, which take some inspiration from evolutionary systems in the natural world. In recent years, a new class of EAs called estimation of distribution algorithms (EDAs) has emerged based on probabilistic modeling of the search space. Instances of EDAs include population based incremental learning (PBIL) algorithm. The PBIL algorithm is an easy to understand heuristic optimization technique that is inspired by the genetic algorithm and the competitive learning paradigm. PBIL includes many of the features from the genetic algorithm such as binary string representation, the notion of individuals, fitness measures and mutations. Contrary to the genetic algorithm it does not maintain a population of individuals but instead PBIL contains a probability vector. At each generation a new population of individuals is sampled according to the probabilities specified in the probability vector. The population is evaluated and the probability vector is updated by dragging it towards the best individual in the population. In recent years, the investigation of synchronization of chaotic systems has attracted much attention of researchers. Chaos synchronization has been applied in many fields such as secure communication, chemical, engineering, and biological systems, among others. This paper presents the synchronization of two identical discrete chaotic systems subject the different initial conditions by designing a proportional-integral-derivative (PID) controller. In addition, the tuning of the PID controller based on a modified PBIL algorithm using similarity analysis is also investigated in this paper. Simulation results show the good performance of the modified PBIL algorithm for synchronization of chaotic systems.