Abstract Due to the shortcomings of GA in path planning, there are too many control variables and it is easy fall into local areas. We introduce idea a gene bank store new chromosomes generated each time bank. The linear regression proposed predict probability crossover mutation next generation. At same time, method increasing chromosome diversity proposed, which avoids problem that difficult s...

Many real-world scientific and engineering applications involve finding solutions to “hard” Multiobjective Optimization Problems (MOPs). Genetic Algorithms (GAs) can be extended to find acceptable MOP Pareto solutions. The intent of this discussion is to illustrate that modifications made to the Multi-Objective messy GA (MOMGA) have further improved the efficiency of the algorithm. The MOMGA is...

We use a genetic algorithm to attack the receiver function inversion problem where the objective is to retrieve shear wave velocities of rock layers as a function of depth. The receiver function inversion is a non–linear, multi–modal, and multi–parameter optimization problem suited for genetic algorithms. We implement a three operator genetic algorithm consisting of selection, single–point cros...

in this paper, multi-objective uniform-diversity genetic algorithm (muga) with a diversity preserving mechanism called the ε-elimination algorithm is used for pareto optimization of 5-degree of freedom vehicle vibration model considering the five conflicting functions simultaneously. the important conflicting objective functions that have been considered in this work are, namely, vertical accel...

multi-criteria shortest path problems (mspp) are called as np-hard. for mspps, a unique solution for optimizing all the criteria simultaneously will rarely exist in reality. algorithmic and approximation schemes are available to solve these problems; however, the complexity of these approaches often prohibits their implementation on real-world applications. this paper describes the development ...