GENETIC ALGORITHM WITH FUZZY FITNESS EVALUATION

Using a fuzzy estimator to evaluate the fitness of chromosomes in a genetic algorithm and adaptively training it in the evolutionary process, the genetic algorithm with fuzzy fitness evaluation is proposed to reduce the computation time of the algorithm. An analysis on the optimization performance of the proposed algorithm shows that it maintains good performance with its computation time saved. Finally, simulation results on design of a fuzzy controller are presented.

An improved genetic algorithm for searching for pollution sources

As an optimization method that has experienced rapid development over the past 20 years, the genetic algorithm has been successfully applied in many fields, but it requires repeated searches based on the characteristics of high-speed computer calculation and conditions of the known relationship between the objective function and independent variables. There are several hundred generations of evolvement, but the functional relationship is unknown in pollution source searches. Therefore, the genetic algorithm cannot be used directly. Certain improvements need to be made based on the actual situation, so that the genetic algorithm can adapt to the actual conditions of environmental problems, and can be used in environmental monitoring and environmental quality assessment. Therefore, a series of methods are proposed for the improvement of the genetic algorithm： （1） the initial generation of individual groups should be artificially set and move from lightly polluted areas to heavily polluted areas; （2） intervention measures should be introduced in the competition between individuals; （3） guide individuals should be added; and （4） specific improvement programs should be put forward. Finally, the scientific rigor and rationality of the improved genetic algorithm are proven through an example.

Neural network and genetic algorithm based global path planning in a static environment

Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.

A novel immune genetic algorithm based on quasi secondary response

Combining the advantages of a genetic algorithm and an artificial immune system, a novel genetic algorithm named immune genetic algorithm based on quasi secondary response （IGA QSR） is proposed. IGA QSR employs a database to simulate the standard secondary response and the quasi secondary response. Elitist strategy, automatic extinction, clonal propagation, diversity guarantee, and selection based on comprehensive fitness are also used in the process of IGA QSR. Theoretical analysis, numerical examples of three benchmark mathematical optimization problems and a trave ling salesman problem all demonstrate that IGA-QSR is more effective not only on convergence speed but also on convergence probability than a simple genetic algorithm with the elitist strategy （ SGA ES）. Besides, IGA QSR allows the designers to stop and restart the optimization process freely with out losing the best results that have already been obtained. These properties make IGA QSR be a fea sible, effective and robust search algorithm for complex engineering problems.

Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm

The three-layer forward neural networks are used to establish the inverse kinematics models of robot manipulators. The fuzzy genetic algorithm based on the linear scaling of the fitness value is presented to update the weights of neural networks. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the proposed method improves considerably the precision of the inverse kinematics solutions for robot manipulators and guarantees a rapid global convergence and overcomes the drawbacks of SGA and the BP algorithm.

Composite multiobjective optimization beamforming based on genetic algorithms

All the parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms （GAs）. Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.

An adaptive genetic algorithm for solving bilevel linear programming problem

Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems. Various methods are proposed for solving this problem. Of all the algorithms, the ge- netic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes maybe infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.

Generalized Self-Adaptive Genetic Algorithms

In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.

A Highly Effective DPA Attack Method Based on Genetic Algorithm

As one of the typical method for side channel attack,DPA has become a serious trouble for the security of encryption algorithm implementation.The potential capability of DPA attack induces researchers making a lot of efforts in this area,which significantly improved the attack efficiency of DPA.However,most of these efforts were made based on the hypothesis that the gathered power consumption data from the target device were stable and low noise.If large deviation happens in part of the power consumption data sample,the efficiency of DPA attack will be reduced rapidly.In this work,a highly efficient method for DPA attack is proposed with the inspiration of genetic algorithm.Based on the designed fitness function,power consumption data that is stable and less noisy will be selected and the noisy ones will be eliminated.In this way,not only improves the robustness and efficiency of DPA attack,but also reduces the number of samples needed.With experiments on block cipher algorithms of DES and SM4,10%and 12.5%of the number of power consumption curves have been reduced in average with the proposed DPAG algorithm compared to original DPA attack respectively.The high efficiency and correctness of the proposed algorithm and novel model are proved by experiments.

Multi-path planning algorithm based on fitness sharing and species evolution

A new algorithm is proposed for underwater vehicles multi-path planning. This algorithm is based on fitness sharing genetic algorithm, clustering and evolution of multiple populations, which can keep the diversity of the solution path, and decrease the operating time because of the independent evolution of each subpopulation. The multi-path planning algorithm is demonstrated by a number of two-dimensional path planning problems. The results show that the multi-path planning algorithm has the following characteristics: high searching capability, rapid convergence and high reliability.

Study on Coal Consumption Curve Fitting of the Thermal Power Based on Genetic Algorithm

Coal consumption curve of the thermal power plant can reflect the function relationship between the coal consumption of unit and load, which plays a key role for research on unit economic operation and load optimal dispatch. Now get coal consumption curve is generally obtained by least square method, but which are static curve and these curves remain unchanged for a long time, and make them are incompatible with the actual operation situation of the unit. Furthermore, coal consumption has the characteristics of typical nonlinear and time varying, sometimes the least square method does not work for nonlinear complex problems. For these problems, a method of coal consumption curve fitting of the thermal power plant units based on genetic algorithm is proposed. The residual analysis method is used for data detection;quadratic function is employed to the objective function;appropriate parameters such as initial population size, crossover rate and mutation rate are set;the unit’s actual coal consumption curves are fitted, and comparing the proposed method with least squares method, the results indicate that fitting effect of the former is better than the latter, and further indicate that the proposed method to do curve fitting can best approximate known data in a certain significance, and they can real-timely reflect the interdependence between power output and coal consumption.

A Simple Application and Design of Genetic Algorithm in Card Problem

According to traditional card problem solving which is based on the idea of genetic algorithm(GA),a set of algorithms is designed to find final solution.For each process in genetic algorithm,including choices of fitness function,parameters determination and coding scheme selection,classic algorithm is used to realize the various steps,and ultimately to find solution of problems.

A New Clustering Protocol for Wireless Sensor Networks Using Genetic Algorithm Approach

This paper examines the optimization of the lifetime and energy consumption of Wireless Sensor Networks (WSNs). These two competing objectives have a deep influence over the service qualification of networks and according to recent studies, cluster formation is an appropriate solution for their achievement. To transmit aggregated data to the Base Station (BS), logical nodes called Cluster Heads (CHs) are required to relay data from the fixed-range sensing nodes located in the ground to high altitude aircraft. This study investigates the Genetic Algorithm (GA) as a dynamic technique to find optimum states. It is a simple framework that includes a proposed mathematical formula, which increasing in coverage is benchmarked against lifetime. Finally, the implementation of the proposed algorithm indicates a better efficiency compared to other simulated works.

Using Genetic Algorithms for Solving the Comparison-Based Identification Problem of Multifactor Estimation Model

In this paper the statement and the methods for solving the comparison-based structure-parametric identification problem of multifactor estimation model are addressed. A new method that combines heuristics methods with genetic algorithms is proposed to solve the problem. In order to overcome some disadvantages of using the classical utility functions, the use of nonlinear Kolmogorov-Gabor polynomial, which contains in its composition the first as well as higher characteristics degrees and all their possible combinations is proposed in this paper. The use of nonlinear methods for identification of the multifactor estimation model showed that the use of this new technique, using as a utility function the nonlinear Kolmogorov-Gabor polynomial and the use of genetic algorithms to calculate the weights, gives a considerable saving in time and accuracy performance. This method is also simpler and more evident for the decision maker (DM) than other methods.

Optimal Time-Frequency Atom Search Based on Adaptive Genetic Algorithm

Adaptive signal decomposition is an important signal processing method.The chirp-based signal representation,for example,the Gaussian chirplet decomposition,has been an active research topic in the field of signal processing.A main challenge of the Gaussian chirplet decomposition is the numerical implementation of the matching pursuit,which is an adaptive signal decomposition scheme,and the challenge remains an open research topic.In this paper,a new optimal time-frequency atom search method based on the adaptive genetic algorithm is proposed,aiming to the low precision problem of the traditional methods.Firstly,a discrete formula of finite length time-frequency atom sequence is derived.Secondly,an algorithm based on the adaptive genetic algorithm is described in detail.Finally,a simulation is carried out,and the result displays its validity and stability.

Genetic Algorithm Works for Vectoring Image Outlines of Generic Shapes

This work proposes a scheme which helps digitizing hand printed and electronic planar objects or vectorizing the generic shapes. An evolutionary optimization technique namely Genetic Algorithm (GA) is used to solve the problem of curve fitting with a cubic spline function. GA works well for finding the optimal values of shape parameters in the description of the proposed cubic spline. The underlying scheme comprises of various phases including data of the image outlines, detection of corner points, using GA for optimal values of shape parameters, and fitting curve using cubic spline to the detected corner points.

A Genetic Algorithm Approach to Optimize Parameters in Infrared Guidance System

In the infrared guidance system, the gray level threshold is key for target recognition. After thresholding, a target in the binary image is distinguished from the complex background by three recognition features. Using a genetic algorithm, this paper seeks to find the optimal parameters varied with different sub images to compute the adaptive segmentation threshold.The experimental results reveal that the GA paradigm is an efficient and effective method of search.

Gaussian fitting based optimal design of aircraft mission success space using multi-objective genetic algorithm

In order to obtain the optimized aircraft design concept which meets the increasingly complex operation environment at the conceptual design stage,System-of-systems(So S)engineering must be considered.This paper proposes a novel optimization method for the design of aircraft Mission Success Space(MSS)based on Gaussian fitting and Genetic Algorithm(GA)in the So S area.First,the concepts in the design and evaluation of MSS are summarized to introduce the Contribution to System-of-Systems(CSS)by using a conventional effectiveness index,Mission Success Rate(MSR).Then,the mathematic modelling of Gaussian fitting technique is noted as the basis of the optimization work.After that,the proposed optimal MSS design is illustrated by the multiobjective optimization process where GA acts as the search tool to find the best solution(via Pareto front).In the case study,a simulation system of penetration mission was built.The simulation results are collected and then processed by two MSS design schemes(contour and neural network)giving the initial variable space to GA optimization.Based on that,the proposed optimization method is implemented under both schemes whose optimal solutions are compared to obtain the final best design in the case study.

Dynamic Niching Genetic Algorithm with Data Attraction for Automatic Clustering

A genetic clustering algorithm was developed based on dynamic niching with data attraction. The algorithm uses the concept of Coulomb attraction to model the attraction between data points. Then, the niches with data attraction are dynamically identified in each generation to automatically evolve the optimal number of clusters as well as the cluster centers of the data set without using cluster validity functions or a variance-covariance matrix. Therefore, this clustering scheme does not need to pre-specify the number of clusters as in existing methods. Several data sets with widely varying characteristics are used to demonstrate the superiority of this algorithm. Experimental results show that the performance of this clustering algorithm is high, effective, and flexible.