This paper presents an adaptive genetic algorithm (GA) to solve the Vehicle Routing Problem with Time Windows (VRPTW) to near optimal solutions. The algorithm employs a unique decoding scheme with the integer strings. It also automatically adapts the crossover probability and the mutation rate to the changing population dynamics. The adaptive control maintains population diversity at user-defin...

A new image denoising algorithm is proposed. GA-ELM algorithm uses genetic algorithm (GA) to decide weights in the Extreme learning Machine algorithm. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-ELM to do image denoising researching work. Firstly, this paper uses training samples to train GA-ELM as the noise detector. Then, we utili...

The control of pH is important in the chemical industry, poses a difficult problem because of inherent nonliniarities and frequently changing process dynamics. The work described in this paper aims at exploring a technique for producing adaptive fuzzy logic controller (FLC), in which a genetic (GA) is employed to alter membership functions. GA is an adaptive search technique based on natural se...

Neural network adaptive filters are mainly used for the interference cancellation techniques. The gradient based design methods are well developed for the design of neural network adaptive filter but they converge to local minima. This paper describes the global optimization interference cancelling techniques for adaptive filtering of interferences in the corrupted signal. The system is designe...

-A concept of a bi-population scheme for real-coded GAs consisting of an explorer sub-GA and an exploiter sub-GA is proposed. The explorer sub-GA mainly does exploration so as to avoid being trapped in local optima by means of restart mechanism; and the exploiter sub-GA does exploitation by which search can be performed more precisely in the neighborhood of the best solution obtained so far. An...

Constraint handling is one of the major concerns when applying genetic algorithms (GAs) to solve constrained optimization problems. This paper proposes to use the gradient information derived from the constraint set to systematically repair infeasible solutions. The proposed repair procedure is embedded into a simple GA as a special operator. Experiments using 11 benchmark problems are presente...

This paper explains novel CPU scheduling approach for embedded operating Systems. In this approach we have used genetic algorithm (GA). Proposed Adaptive algorithm combines both EDF and GA based algorithms, Basically the new algorithm uses EDF algorithm but when the system becomes overloaded, it will switch to GA based scheduling algorithm. Again, when the overload disappears, the system will s...

This paper presents a new methodology for improving the efficiency and generality of Genetic Algorithms (GA). The methodology provides the novel function of adaptive parameter adjustment during each evolution generation of GA. The important characteristics of the methodology are mainly from the following two aspects: (1) superior performance members in GA are preserved and inferior performance ...

In order to understand heterogeneous behavior amongst agents, empirical data from Learning-to-Forecast (LtF) experiments can be used to construct learning models. This paper follows up on Assenza et al. (2013) by using a Genetic Algorithms (GA) model to replicate the results from their LtF experiment. In this GA model, individuals optimize an adaptive, a trend following and an anchor coefficien...