1812 Words8 Pages

1. Introduction
The most popular technique in evolutionary computation research has been the genetic algorithm. In the traditional genetic algorithm, the representation used is a fixed-length bit string. Each position in the string is assumed to represent a particular feature of an individual, and the value stored in that position represents how that feature is expressed in the solution. Usually, the string is “evaluated as a collection of structural features of a solution that have little or no interactions”. The analogy may be drawn directly to genes in biological organisms. Each gene represents an entity that is structurally independent of other genes. The main reproduction operator used is bit-string crossover, in which two strings are used as parents and new individuals are formed by swapping a*…show more content…*

Advantages and Limitations of Genetic Algorithms The advantages of genetic algorithm includes: 1. Parallelism 2. Liability 3. Solution space is wider 4. The fitness landscape is complex 5. Easy to discover global optimum 6. The problem has multi objective function 7. Only uses function evaluations. 8. Easily modified for different problems. 9. Handles noisy functions well. 10. Handles large, poorly understood search spaces easily 11. Good for multi-modal problems Returns a suite of solutions. 12. Very robust to difficulties in the evaluation of the objective function. The limitation of genetic algorithm includes: 1. The problem of identifying fitness function 2. Definition of representation for the problem 3. Premature convergence occurs 4. The problem of choosing the various parameters like the size of the population, mutation rate, cross over rate, the selection method and its strength. 5. Cannot use gradients. 6. Cannot easily incorporate problem specific information 7. Not good at identifying local optima 8. No effective terminator. 9. Not effective for smooth unimodal functions 10. Needs to be coupled with a local search

Advantages and Limitations of Genetic Algorithms The advantages of genetic algorithm includes: 1. Parallelism 2. Liability 3. Solution space is wider 4. The fitness landscape is complex 5. Easy to discover global optimum 6. The problem has multi objective function 7. Only uses function evaluations. 8. Easily modified for different problems. 9. Handles noisy functions well. 10. Handles large, poorly understood search spaces easily 11. Good for multi-modal problems Returns a suite of solutions. 12. Very robust to difficulties in the evaluation of the objective function. The limitation of genetic algorithm includes: 1. The problem of identifying fitness function 2. Definition of representation for the problem 3. Premature convergence occurs 4. The problem of choosing the various parameters like the size of the population, mutation rate, cross over rate, the selection method and its strength. 5. Cannot use gradients. 6. Cannot easily incorporate problem specific information 7. Not good at identifying local optima 8. No effective terminator. 9. Not effective for smooth unimodal functions 10. Needs to be coupled with a local search

Related

## Limitations And Limitations Of Evolutionary Algorithms

951 Words | 4 PagesLIMITATIONS OF EVOLUTIONARY ALGORITHMS Evolutionary algorithms are very promising problem solving techniques but at the same time, they have a few limitations that can result in loss of efficiency. • Difficult parameter tuning: Any implementation of an Evolutionary algorithms will require the specification of various parameters, such as population size, mutation rate, and maximum run time, as well as the design of selection, recombination, and mutation procedures. Finding effective choices for these

## Fuzzy Multi Objective Particle Swarm Optimization

1151 Words | 5 PagesThis approach propose a “fuzzy-multi-objective particle swarm optimization” (FMOPSO) for solving TCQT problem. The parameters of cost, time and quality are defined by fuzzy numbers and a “fuzzy multi attribute utility” procedure is used with limits of fuzzy arithmetic operation to adopt and evaluate the selected construction methods. The proposed method is justified and implemented through computational analyses. The above method suggests optimal combination of construction method with the large

## Permanent Magnet Brushless Direct Current Motors

2042 Words | 9 PagesPermanent magnet brushless direct current motors Nowadays, because of the fast evolution of electronic device, The recent developments in permanent magnet materials, solid state devices and microelectronic have led to the appearance of a new energy efficient drives using permanent magnet brushless direct current motors (PMBLDCM). Brushless direct current (BLDC) motors are preferred as small horsepower control motors because their efficiency is very high, the operation is in a silent mode, has a

## A New Energy Efficient Drives Using Permanent Magnet Brushless Direct Current Motors ( Pmbldcm )

2036 Words | 9 PagesNowadays, because of the fast evolution of electronic device, The recent developments in permanent magnet materials, solid state devices and microelectronic have led to the appearance of a new energy efficient drives using permanent magnet brushless direct current motors (PMBLDCM). Brushless direct current (BLDC) motors are preferred as small horsepower control motors because their efficiency is very high, the operation is in a silent mode, has a compact form, contains high precision, low maintenance

## Is Data Mining A Detection Process?

2438 Words | 10 Pagesdifferent-groups varies in its characteristics. Such dissimilar groups are labeled as clusters. These clusters consist of several analogous data or objects relating to a referral point [15]. Darwin 's theory inspired Genetic algorithms development. GA is evolved to resolve solution to this problem. Algorithm starts with a set of solutions (chromosomes) known as population. Outcomes from a population are taken and used to structure a new population. This is aggravated by optimism, and the new population will better

## Evaluating The Potential Quality Loss Cost

1692 Words | 7 Pagesin planning. Another research highlights using linear and integer programming algorithms for resource optimization while scheduling of project, thereby analyzing potential time-cost tradeoff. In a different approach, researcher used fuzzy multi-attribute utility methodology and justifying it with computational analysis to provide an alternate solution to the problem. A research focusing on principle of genetic algorithms, results in development of VBA macro programs which uses the total project cost

## Physical Effects Of Virtual Reality

1633 Words | 7 Pagestime to create an environment which is indistinguishable from the real thing, for example, a 3D walk through of a building which can a year or more to complete. LIMITATIONS OF

## The Swarm Based Routing Algorithms

1441 Words | 6 PagesBIOINSPIRED SWARM BASED ROUTING ALGORITHMS IN VANETs Arshpreet Kaur*, Er. Navroz Kaur Kahlon** *(Computer Engineering Department, UCOE, Punjabi University, Patiala) ** (Asst. Prof. Computer Engineering Department, Punjabi University, Patiala) (Email: *arshrai90@gmail.com, **Kahlon.navroz3@gmail.com) Abstract-Vehicular Ad-hoc Networks (VANETs) play main role in the design and development of the Intelligent Transportation Systems (ITS) who improves the road safety and transportation productivity

## Load Balancing

4007 Words | 17 Pagestelecommunication has perhaps been to bring us humans closer to one another. In doing so, one of the greatest contributors has been cellular phone technology. Thus, we tried to contribute, to our limitations, to the development of the cellular network. Cellular technology not only connects people, it is also playing a colossal role in boosting the economy of Bangladesh. With the increasing popularity of cellular phones and arrival of giant phone companies

## Evolution And Innovation And The Development Of Aerospace Design

3604 Words | 15 PagesIntroduction Evolution and innovation are two of the most important factors in the development of aerospace design. When faced with a design problem the decision has to be made whether to develop the current idea, or whether to explore a new idea. The decision chosen will usually be a mixture of both. Brief History of Optimisation Optimisation is an extremely old method and was used as far back as when Newton used it to calculate maximum and minimum values for functions. One of the first times that

### Limitations And Limitations Of Evolutionary Algorithms

951 Words | 4 Pages### Fuzzy Multi Objective Particle Swarm Optimization

1151 Words | 5 Pages### Permanent Magnet Brushless Direct Current Motors

2042 Words | 9 Pages### A New Energy Efficient Drives Using Permanent Magnet Brushless Direct Current Motors ( Pmbldcm )

2036 Words | 9 Pages### Is Data Mining A Detection Process?

2438 Words | 10 Pages### Evaluating The Potential Quality Loss Cost

1692 Words | 7 Pages### Physical Effects Of Virtual Reality

1633 Words | 7 Pages### The Swarm Based Routing Algorithms

1441 Words | 6 Pages### Load Balancing

4007 Words | 17 Pages### Evolution And Innovation And The Development Of Aerospace Design

3604 Words | 15 Pages