In knowledge discovery, Genetic Algorithms have been used for classification, model selection, and other optimization tasks. However, behavior and performance of genetic algorithms are directly affected by the values of their input parameters, while poor parameter settings usually lead to several problems such as the premature convergence. Adaptive techniques have been suggested for adjusting t...

The traditional particle swarm optimization algorithm is fast and efficient, but it easy to fall into a local optimum. An improved PSO proposed applied in 3D path planning of UAV solve the problem. Improvement methods are described as follows: combining with genetic (GA), setting dynamic inertia weight, adding sigmoid function improve crossover mutation probability algorithm, changing selection...

This paper proposes a formal definition of influence in Bayesian reasoning, based on the notions of state (as probability distribution), predicate, validity and conditioning. Our approach highlights how conditioning a joint entwined/entangled state with a predicate on one of its components has ‘crossover’ influence on the other components. We use the total variation metric on probability distri...

This paper presents an improved genetic algorithm to solve the materialized view selection problem under query cost constraints. The algorithm dynamically changes the crossover probability and mutation probability in the process of genetic. In this way, it can not only maintain the population diversity, but also ensure the convergence of the genetic algorithm. So it effectively improves the opt...

This paper addresses production-inventory problem for the manufacturer by explicitly taking into account multistage and varying demand. A nonlinear hybrid integer constrained optimization is modeled to minimize the total cost including setup cost and holding cost in the planning horizon. A genetic algorithm is developed for the problem. A series of computational experiments with different sizes...

A novel pareto-optimization technique based on newly developed hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) is presented in this paper. In HFMOEA, two significant parameters such as crossover probability (PC) and mutation probability (PM) are dynamically varied during optimization based on the output of a fuzzy controller for improving its convergence performance by guiding the ...

This paper presents the solution of economic power dispatch (EPD) through the application of genetic algorithm (GA) and the taguchi method. The economic power dispatch is a non-linear optimisation problem with several constraints. The objective of the proposed genetic algorithm combined taguchi method is to choose the most efficient combination of Pc (crossover probability), Pm (mutation probab...

To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks....

We study toy aging processes in hierarchically decomposed phase spaces where the equilibrium probability distributions are multifractal. We found that the an auto-correlation function, survival-return probability, shows crossover behavior from a power law t−x in the quasi-equilibrium regime (t ≪ tw) to another power law t −λ (λ ≥ x) in the off-equilibrium regime (t ≫ tw) obeying a simple t/tw s...

due to convergence; not \premature convergence", or \convergence to a suboptimal string", but simple convergence. Once the population converges, either across the entire string or in a particular region, various GA mechanisms missre. The series of missteps traced in early attempts to connrm the hypotheses hint at the complications that will inevitably arise with the introduction of endogenous t...